Repository: tiev-tongji/LOG-LIO Branch: main Commit: a2f49d66b9e3 Files: 75 Total size: 13.7 MB Directory structure: gitextract_jallgs7z/ ├── CMakeLists.txt ├── LICENSE ├── Log/ │ ├── dbg.txt │ ├── fast_lio_time_log.csv │ ├── fast_lio_time_log_analysis.m │ ├── guide.md │ ├── imu.txt │ ├── mat_out.txt │ ├── mat_pre.txt │ ├── plot.py │ └── pos_log.txt ├── README.md ├── config/ │ ├── m2dgr/ │ │ ├── ring32_M_0.xml │ │ ├── ring32_M_1.xml │ │ ├── ring32_M_2.xml │ │ ├── ring32_M_3.xml │ │ ├── ring32_M_4.xml │ │ ├── ring32_M_5.xml │ │ ├── ring32_M_6.xml │ │ ├── ring32_M_7.xml │ │ ├── ring32_M_8.xml │ │ └── ring32_v.xml │ ├── ouster64.yaml │ ├── rs128.yaml │ ├── velodyne.yaml │ ├── velodyne_m2dgr.yaml │ ├── viral/ │ │ ├── ring16_M_0.xml │ │ ├── ring16_M_1.xml │ │ ├── ring16_M_2.xml │ │ ├── ring16_M_3.xml │ │ ├── ring16_M_4.xml │ │ ├── ring16_M_5.xml │ │ ├── ring16_M_6.xml │ │ ├── ring16_M_7.xml │ │ ├── ring16_M_8.xml │ │ └── ring16_v.xml │ └── viral.yaml ├── include/ │ ├── Exp_mat.h │ ├── IKFoM_toolkit/ │ │ ├── esekfom/ │ │ │ ├── esekfom.hpp │ │ │ └── util.hpp │ │ └── mtk/ │ │ ├── build_manifold.hpp │ │ ├── src/ │ │ │ ├── SubManifold.hpp │ │ │ ├── mtkmath.hpp │ │ │ └── vectview.hpp │ │ ├── startIdx.hpp │ │ └── types/ │ │ ├── S2.hpp │ │ ├── SOn.hpp │ │ ├── vect.hpp │ │ └── wrapped_cv_mat.hpp │ ├── common_lib.h │ ├── ikd-Tree/ │ │ ├── ikd_Tree.cpp │ │ └── ikd_Tree.h │ ├── matplotlibcpp.h │ ├── point_type.h │ ├── so3_math.h │ ├── use-ikfom.hpp │ └── voxel_map_util.hpp ├── launch/ │ ├── gdb_debug_example.launch │ ├── mapping_m2dgr.launch │ ├── mapping_ouster64.launch │ ├── mapping_rs.launch │ ├── mapping_velodyne.launch │ └── mapping_viral.launch ├── msg/ │ └── Pose6D.msg ├── package.xml └── src/ ├── IMU_Processing.hpp ├── cloud_process.hpp ├── laserMapping.cpp ├── preprocess.cpp ├── preprocess.h ├── ring_fals/ │ ├── Image_normals.hpp │ ├── precomp.h │ ├── range_image.cpp │ └── range_image.h └── tic_toc.h ================================================ FILE CONTENTS ================================================ ================================================ FILE: CMakeLists.txt ================================================ cmake_minimum_required(VERSION 2.8.3) project(log_lio) SET(CMAKE_BUILD_TYPE "Debug") ADD_COMPILE_OPTIONS(-std=c++14 ) ADD_COMPILE_OPTIONS(-std=c++14 ) set( CMAKE_CXX_FLAGS "-std=c++14 -O3" ) add_definitions(-DROOT_DIR=\"${CMAKE_CURRENT_SOURCE_DIR}/\") set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -fexceptions" ) set(CMAKE_CXX_STANDARD 14) set(CMAKE_CXX_STANDARD_REQUIRED ON) set(CMAKE_CXX_EXTENSIONS OFF) set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++14 -pthread -std=c++0x -std=c++14 -fexceptions") message("Current CPU archtecture: ${CMAKE_SYSTEM_PROCESSOR}") if(CMAKE_SYSTEM_PROCESSOR MATCHES "(x86)|(X86)|(amd64)|(AMD64)" ) include(ProcessorCount) ProcessorCount(N) message("Processer number: ${N}") if(N GREATER 4) add_definitions(-DMP_EN) add_definitions(-DMP_PROC_NUM=3) message("core for MP: 3") elseif(N GREATER 3) add_definitions(-DMP_EN) add_definitions(-DMP_PROC_NUM=2) message("core for MP: 2") else() add_definitions(-DMP_PROC_NUM=1) endif() else() add_definitions(-DMP_PROC_NUM=1) endif() find_package(OpenMP QUIET) set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}") set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OpenMP_C_FLAGS}") find_package(PythonLibs REQUIRED) find_path(MATPLOTLIB_CPP_INCLUDE_DIRS "matplotlibcpp.h") find_package(catkin REQUIRED COMPONENTS geometry_msgs nav_msgs sensor_msgs roscpp rospy std_msgs pcl_ros tf # livox_ros_driver message_generation eigen_conversions ) find_package(Eigen3 REQUIRED) find_package(PCL 1.7 REQUIRED) ## set OpenCV DIR if ($ENV{ROS_DISTRO} STREQUAL "kinetic") Set(OpenCV_DIR "/home/hk/opencv-3.3.1/build") # important find opencv version else() Set(OpenCV_DIR "/home/autolab/opencv-3.2.0/build") # important find opencv version, Neotic endif () find_package(OpenCV 3.2 QUIET) message(Eigen: ${EIGEN3_INCLUDE_DIR}) include_directories( ${catkin_INCLUDE_DIRS} ${EIGEN3_INCLUDE_DIR} ${PCL_INCLUDE_DIRS} ${PYTHON_INCLUDE_DIRS} ${OpenCV_INCLUDE_DIRS} include ) add_message_files( FILES Pose6D.msg ) generate_messages( DEPENDENCIES geometry_msgs ) catkin_package( CATKIN_DEPENDS geometry_msgs nav_msgs roscpp rospy std_msgs message_runtime DEPENDS EIGEN3 PCL INCLUDE_DIRS ) add_executable(loglio_mapping src/laserMapping.cpp include/ikd-Tree/ikd_Tree.cpp src/preprocess.cpp src/ring_fals/range_image.cpp ) target_link_libraries(loglio_mapping ${catkin_LIBRARIES} ${PCL_LIBRARIES} ${PYTHON_LIBRARIES} ${OpenCV_LIBRARIES} ) target_include_directories(loglio_mapping PRIVATE ${PYTHON_INCLUDE_DIRS}) ================================================ FILE: LICENSE ================================================ GNU GENERAL PUBLIC LICENSE Version 2, June 1991 Copyright (C) 1989, 1991 Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed. 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Here is a sample; alter the names: Yoyodyne, Inc., hereby disclaims all copyright interest in the program `Gnomovision' (which makes passes at compilers) written by James Hacker. , 1 April 1989 Ty Coon, President of Vice This General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Lesser General Public License instead of this License. ================================================ FILE: Log/dbg.txt ================================================ ================================================ FILE: Log/fast_lio_time_log.csv ================================================ time_stamp, total time, scan point size, incremental time, search time, delete size, delete time, tree size st, tree size end, add point size, preprocess time ================================================ FILE: Log/fast_lio_time_log_analysis.m ================================================ clear close all Color_red = [0.6350 0.0780 0.1840]; Color_blue = [0 0.4470 0.7410]; Color_orange = [0.8500 0.3250 0.0980]; Color_green = [0.4660 0.6740 0.1880]; Color_lightblue = [0.3010 0.7450 0.9330]; Color_purple = [0.4940 0.1840 0.5560]; Color_yellow = [0.9290 0.6940 0.1250]; fast_lio_ikdtree = csvread("./fast_lio_time_log.csv",1,0); timestamp_ikd = fast_lio_ikdtree(:,1); timestamp_ikd = timestamp_ikd - min(timestamp_ikd); total_time_ikd = fast_lio_ikdtree(:,2)*1e3; scan_num = fast_lio_ikdtree(:,3); incremental_time_ikd = fast_lio_ikdtree(:,4)*1e3; search_time_ikd = fast_lio_ikdtree(:,5)*1e3; delete_size_ikd = fast_lio_ikdtree(:,6); delete_time_ikd = fast_lio_ikdtree(:,7) * 1e3; tree_size_ikd_st = fast_lio_ikdtree(:,8); tree_size_ikd = fast_lio_ikdtree(:,9); add_points = fast_lio_ikdtree(:,10); fast_lio_forest = csvread("fast_lio_time_log.csv",1,0); fov_check_time_forest = fast_lio_forest(:,5)*1e3; average_time_forest = fast_lio_forest(:,2)*1e3; total_time_forest = fast_lio_forest(:,6)*1e3; incremental_time_forest = fast_lio_forest(:,3)*1e3; search_time_forest = fast_lio_forest(:,4)*1e3; timestamp_forest = fast_lio_forest(:,1); % Use slide window to calculate average L = 1; % Length of slide window for i = 1:length(timestamp_ikd) if (i 0); search_time_ikd = search_time_ikd(index_ikd); index_forest = find(search_time_forest > 0); search_time_forest = search_time_forest(index_forest); t = nexttile; hold on; boxplot_data_ikd = [incremental_time_ikd,total_time_ikd]; boxplot_data_forest = [incremental_time_forest,total_time_forest]; Colors_ikd = [Color_blue;Color_blue;Color_blue]; Colors_forest = [Color_orange;Color_orange;Color_orange]; % xticks([3,8,13]) h_search_ikd = boxplot(search_time_ikd,'Whisker',50,'Positions',1,'Colors',Color_blue,'Widths',0.3); h_search_forest = boxplot(search_time_forest,'Whisker',50,'Positions',1.5,'Colors',Color_orange,'Widths',0.3); h_ikd = boxplot(boxplot_data_ikd,'Whisker',50,'Positions',[3,5],'Colors',Color_blue,'Widths',0.3); h_forest = boxplot(boxplot_data_forest,'Whisker',50,'Positions',[3.5,5.5],'Colors',Color_orange,'Widths',0.3); ax2 = gca; ax2.YAxis.Scale = 'log'; xlim([0.5,6.0]) ylim([0.0008,100]) xticks([1.25 3.25 5.25]) xticklabels({'Nearest Search',' Incremental Updates','Total Time'}); yticks([1e-3,1e-2,1e-1,1e0,1e1,1e2]) ax2.YAxis.FontSize = 12; ax2.XAxis.FontSize = 14.5; % ax.XAxis.FontWeight = 'bold'; ylabel('Run Time/ms','FontSize',14,'FontName','Times New Roman') box_vars = [findall(h_search_ikd,'Tag','Box');findall(h_ikd,'Tag','Box');findall(h_search_forest,'Tag','Box');findall(h_forest,'Tag','Box')]; for j=1:length(box_vars) if (j<=3) Color = Color_blue; else Color = Color_orange; end patch(get(box_vars(j),'XData'),get(box_vars(j),'YData'),Color,'FaceAlpha',0.25,'EdgeColor',Color); end Lg = legend(box_vars([1,4]), {'ikd-Tree','ikd-Forest'},'Location',[0.6707 0.4305 0.265 0.07891],'fontsize',14,'fontname','Times New Roman'); grid on set(gca,'YMinorGrid','off') nexttile; hold on; grid on; box on; set(gca,'FontSize',12,'FontName','Times New Roman') plot(timestamp_ikd, alpha_bal_ikd,'-','Color',Color_blue,'LineWidth',1.2); plot(timestamp_ikd, alpha_del_ikd,'--','Color',Color_orange, 'LineWidth', 1.2); plot(timestamp_ikd, 0.6*ones(size(alpha_bal_ikd)), ':','Color','black','LineWidth',1.2); lg = legend("\alpha_{bal}", "\alpha_{del}",'location',[0.7871 0.1131 0.1433 0.069],'fontsize',14,'fontname','Times New Roman') title("Re-balancing Criterion",'FontSize',16,'FontName','Times New Roman') xlabel("time/s",'FontSize',16,'FontName','Times New Roman') yl = ylabel("\alpha",'FontSize',15, 'Position',[285.7 0.4250 -1]) xlim([32,390]); ylim([0,0.85]); ax3 = gca; ax3.YAxis.FontSize = 12; ax3.XAxis.FontSize = 12; % print('./Figures/fastlio_exp_combine','-depsc','-r1200') % exportgraphics(f,'./Figures/fastlio_exp_combine_1.pdf','ContentType','vector') ================================================ FILE: Log/guide.md ================================================ Here saved the debug records which can be drew by the ../Log/plot.py. The record function can be found frm the MACRO: DEBUG_FILE_DIR(name) in common_lib.h. ================================================ FILE: Log/imu.txt ================================================ ================================================ FILE: Log/mat_out.txt ================================================ ================================================ FILE: Log/mat_pre.txt ================================================ ================================================ FILE: Log/plot.py ================================================ # import matplotlib # matplotlib.use('Agg') import numpy as np import matplotlib.pyplot as plt #######for ikfom fig, axs = plt.subplots(4,2) lab_pre = ['', 'pre-x', 'pre-y', 'pre-z'] lab_out = ['', 'out-x', 'out-y', 'out-z'] plot_ind = range(7,10) a_pre=np.loadtxt('mat_pre.txt') a_out=np.loadtxt('mat_out.txt') time=a_pre[:,0] axs[0,0].set_title('Attitude') axs[1,0].set_title('Translation') axs[2,0].set_title('Extrins-R') axs[3,0].set_title('Extrins-T') axs[0,1].set_title('Velocity') axs[1,1].set_title('bg') axs[2,1].set_title('ba') axs[3,1].set_title('Gravity') for i in range(1,4): for j in range(8): axs[j%4, j/4].plot(time, a_pre[:,i+j*3],'.-', label=lab_pre[i]) axs[j%4, j/4].plot(time, a_out[:,i+j*3],'.-', label=lab_out[i]) for j in range(8): # axs[j].set_xlim(386,389) axs[j%4, j/4].grid() axs[j%4, j/4].legend() plt.grid() #######for ikfom####### #### Draw IMU data # fig, axs = plt.subplots(2) # imu=np.loadtxt('imu.txt') # time=imu[:,0] # axs[0].set_title('Gyroscope') # axs[1].set_title('Accelerameter') # lab_1 = ['gyr-x', 'gyr-y', 'gyr-z'] # lab_2 = ['acc-x', 'acc-y', 'acc-z'] # for i in range(3): # # if i==1: # axs[0].plot(time, imu[:,i+1],'.-', label=lab_1[i]) # axs[1].plot(time, imu[:,i+4],'.-', label=lab_2[i]) # for i in range(2): # # axs[i].set_xlim(386,389) # axs[i].grid() # axs[i].legend() # plt.grid() # #### Draw time calculation # plt.figure(3) # fig = plt.figure() # font1 = {'family' : 'Times New Roman', # 'weight' : 'normal', # 'size' : 12, # } # c="red" # a_out1=np.loadtxt('Log/mat_out_time_indoor1.txt') # a_out2=np.loadtxt('Log/mat_out_time_indoor2.txt') # a_out3=np.loadtxt('Log/mat_out_time_outdoor.txt') # # n = a_out[:,1].size # # time_mean = a_out[:,1].mean() # # time_se = a_out[:,1].std() / np.sqrt(n) # # time_err = a_out[:,1] - time_mean # # feat_mean = a_out[:,2].mean() # # feat_err = a_out[:,2] - feat_mean # # feat_se = a_out[:,2].std() / np.sqrt(n) # ax1 = fig.add_subplot(111) # ax1.set_ylabel('Effective Feature Numbers',font1) # ax1.boxplot(a_out1[:,2], showfliers=False, positions=[0.9]) # ax1.boxplot(a_out2[:,2], showfliers=False, positions=[1.9]) # ax1.boxplot(a_out3[:,2], showfliers=False, positions=[2.9]) # ax1.set_ylim([0, 3000]) # ax2 = ax1.twinx() # ax2.spines['right'].set_color('red') # ax2.set_ylabel('Compute Time (ms)',font1) # ax2.yaxis.label.set_color('red') # ax2.tick_params(axis='y', colors='red') # ax2.boxplot(a_out1[:,1]*1000, showfliers=False, positions=[1.1],boxprops=dict(color=c),capprops=dict(color=c),whiskerprops=dict(color=c)) # ax2.boxplot(a_out2[:,1]*1000, showfliers=False, positions=[2.1],boxprops=dict(color=c),capprops=dict(color=c),whiskerprops=dict(color=c)) # ax2.boxplot(a_out3[:,1]*1000, showfliers=False, positions=[3.1],boxprops=dict(color=c),capprops=dict(color=c),whiskerprops=dict(color=c)) # ax2.set_xlim([0.5, 3.5]) # ax2.set_ylim([0, 100]) # plt.xticks([1,2,3], ('Outdoor Scene', 'Indoor Scene 1', 'Indoor Scene 2')) # # # print(time_se) # # # print(a_out3[:,2]) # plt.grid() # plt.savefig("time.pdf", dpi=1200) plt.show() ================================================ FILE: Log/pos_log.txt ================================================ ================================================ FILE: README.md ================================================ # LOG-LIO ---------------------------- Our recent work [LOG-LIO2](https://github.com/tiev-tongji/LOG-LIO2) is now available! The trajectory file will be saved in **TUM** format in the file named "/Log/target_path.txt". And a more detailed readme is coming soon! **Related video:** [Ring FALS](https://youtu.be/cxTLywI7X7M). ## 1. Prerequisites ### 1.1 **Ubuntu** and **ROS** **Ubuntu >= 16.04** For **Ubuntu 18.04 or higher**, the **default** PCL and Eigen is enough for FAST-LIO to work normally. [//]: # () [//]: # (ROS >= Melodic. [ROS Installation](http://wiki.ros.org/ROS/Installation)) ### 1.2 Ring FALS Normal Estimator Compile follow [Ring FALS normal estimator](https://github.com/tiev-tongji/RingFalsNormal). ## 2. Build Clone the repository and catkin_make: ``` cd ~/$A_ROS_DIR$/src git clone https://github.com/tiev-tongji/LOG-LIO.git cd .. catkin_make source devel/setup.bash ``` ## 3. run We conduct experiments on the [M2DGR](https://github.com/SJTU-ViSYS/M2DGR) and [NTU VIRAL](https://github.com/ntu-aris/ntu_viral_dataset) datasets. The corresponding launch and yaml file is provided. Note that the timestamp in the NTU VARIL dataset is the **_end time_**. ``` cd ~/$LOG_LIO_ROS_DIR$ source devel/setup.bash # for the M2DGR dataset roslaunch log_lio mapping_m2dgr.launch # for the NTU VIRAL dataset roslaunch log_lio mapping_viral.launch ``` ## 4. Acknowledgments Thanks for LOAM(J. Zhang and S. Singh. LOAM: Lidar Odometry and Mapping in Real-time), [Fast-LIO2](https://github.com/hku-mars/FAST_LIO), [ikd-Tree](https://github.com/hku-mars/ikd-Tree) and [VoxelMap](https://github.com/hku-mars/VoxelMap). ## Citation ``` @article{huang2023log, title={LOG-LIO: A LiDAR-Inertial Odometry with Efficient Local Geometric Information Estimation}, author={Huang, Kai and Zhao, Junqiao and Zhu, Zhongyang and Ye, Chen and Feng, Tiantian}, journal={IEEE Robotics and Automation Letters}, volume={9}, number={1}, pages={459--466}, year={2023}, publisher={IEEE} } ``` ================================================ FILE: config/m2dgr/ring32_M_0.xml ================================================ 32 1800
f
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-7.40149617e-02 2.58819193e-01 -9.63337481e-01 -7.06479251e-02 2.58819193e-01 -9.63568866e-01 -6.73964173e-02 2.58819193e-01 -9.63801622e-01 -6.39727861e-02 2.58819193e-01 -9.63997066e-01 -6.10250533e-02 2.58819193e-01 -9.64172721e-01 -5.81550300e-02 2.58819073e-01 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. -9.65476871e-01 -2.94565745e-02 2.58818895e-01 -9.65552568e-01 -2.68579274e-02 2.58819133e-01 -9.65636551e-01 -2.35829391e-02 2.58819193e-01 -9.65708375e-01 -2.05187816e-02 2.58819133e-01 -9.65750992e-01 -1.83748249e-02 2.58819044e-01 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. ================================================ FILE: config/ouster64.yaml ================================================ common: lid_topic: "/os_cloud_node/points" imu_topic: "/os_cloud_node/imu" time_sync_en: false # ONLY turn on when external time synchronization is really not possible time_offset_lidar_to_imu: 0.0 # Time offset between lidar and IMU calibrated by other algorithms, e.g. LI-Init (can be found in README). # This param will take effect no matter what time_sync_en is. So if the time offset is not known exactly, please set as 0.0 preprocess: lidar_type: 3 # 1 for Livox serials LiDAR, 2 for Velodyne LiDAR, 3 for ouster LiDAR, scan_line: 64 timestamp_unit: 3 # 0-second, 1-milisecond, 2-microsecond, 3-nanosecond. blind: 4 mapping: acc_cov: 0.1 gyr_cov: 0.1 b_acc_cov: 0.0001 b_gyr_cov: 0.0001 fov_degree: 180 det_range: 150.0 extrinsic_est_en: false # true: enable the online estimation of IMU-LiDAR extrinsic extrinsic_T: [ 0.0, 0.0, 0.0 ] extrinsic_R: [1, 0, 0, 0, 1, 0, 0, 0, 1] publish: path_en: false scan_publish_en: true # false: close all the point cloud output dense_publish_en: true # false: low down the points number in a global-frame point clouds scan. scan_bodyframe_pub_en: true # true: output the point cloud scans in IMU-body-frame pcd_save: pcd_save_en: true interval: -1 # how many LiDAR frames saved in each pcd file; # -1 : all frames will be saved in ONE pcd file, may lead to memory crash when having too much frames. ================================================ FILE: config/rs128.yaml ================================================ common: #NCLT # lid_topic: "/points_raw" # imu_topic: "/imu_raw" #KITTI # lid_topic: "/kitti/velo/pointcloud" # imu_topic: "/kitti/oxts/imu" #RS LiDar lid_topic: "/rslidar" imu_topic: "/imu" time_sync_en: false # ONLY turn on when external time synchronization is really not possible preprocess: lidar_type: 4 # 1 for Livox serials LiDAR, 2 for Velodyne LiDAR, 3 for ouster LiDAR, 4 RS scan_line: 128 scan_rate: 10 # only need to be set for velodyne, unit: Hz, blind: 2.5 mapping: acc_cov: 0.1 gyr_cov: 0.1 b_acc_cov: 0.0001 b_gyr_cov: 0.0001 fov_degree: 180 det_range: 100.0 extrinsic_est_en: false # true: enable the online estimation of IMU-LiDAR extrinsic, extrinsic_T: [ 1.494363791111, 0.000030061324, 0.903719820280] extrinsic_R: [ -0.508348737805, 0.861029086113, -0.014507709641, -0.861100925582, -0.508429079519, -0.002251013466, -0.009314329527, 0.011348302346, 0.999892223842] time_delay: 0.0 #time delay from IMU to LiDAR publish: path_en: true scan_publish_en: true # false: close all the point cloud output dense_publish_en: true # false: low down the points number in a global-frame point clouds scan. scan_bodyframe_pub_en: true # true: output the point cloud scans in IMU-body-frame pcd_save: pcd_save_en: true interval: 1 # how many LiDAR frames saved in each pcd file; # -1 : all frames will be saved in ONE pcd file, may lead to memory crash when having too much frames. ================================================ FILE: config/velodyne.yaml ================================================ common: lid_topic: "/velodyne_points" imu_topic: "/handsfree/imu" time_sync_en: false # ONLY turn on when external time synchronization is really not possible time_offset_lidar_to_imu: 0.0 # Time offset between lidar and IMU calibrated by other algorithms, e.g. LI-Init (can be found in README). # This param will take effect no matter what time_sync_en is. So if the time offset is not known exactly, please set as 0.0 preprocess: lidar_type: 2 # 1 for Livox serials LiDAR, 2 for Velodyne LiDAR, 3 for ouster LiDAR, scan_line: 32 scan_rate: 10 # only need to be set for velodyne, unit: Hz, timestamp_unit: 2 # the unit of time/t field in the PointCloud2 rostopic: 0-second, 1-milisecond, 2-microsecond, 3-nanosecond. blind: 2 mapping: acc_cov: 0.1 gyr_cov: 0.1 b_acc_cov: 0.0001 b_gyr_cov: 0.0001 fov_degree: 180 det_range: 100.0 extrinsic_est_en: false # true: enable the online estimation of IMU-LiDAR extrinsic, extrinsic_T: [0.27255, -0.00053, 0.17954] extrinsic_R: [ 1, 0, 0, 0, 1, 0, 0, 0, 1] publish: path_en: true scan_publish_en: true # false: close all the point cloud output dense_publish_en: true # false: low down the points number in a global-frame point clouds scan. scan_bodyframe_pub_en: true # true: output the point cloud scans in IMU-body-frame pcd_save: pcd_save_en: false interval: -1 # how many LiDAR frames saved in each pcd file; # -1 : all frames will be saved in ONE pcd file, may lead to memory crash when having too much frames. ================================================ FILE: config/velodyne_m2dgr.yaml ================================================ common: lid_topic: "/velodyne_points" imu_topic: "/handsfree/imu" time_sync_en: false # ONLY turn on when external time synchronization is really not possible time_offset_lidar_to_imu: 0.0 # Time offset between lidar and IMU calibrated by other algorithms, e.g. LI-Init (can be found in README). # This param will take effect no matter what time_sync_en is. So if the time offset is not known exactly, please set as 0.0 preprocess: lidar_type: 2 # 1 for Livox serials LiDAR, 2 for Velodyne LiDAR, 3 for ouster LiDAR, scan_line: 32 scan_rate: 10 # only need to be set for velodyne, unit: Hz, timestamp_unit: 2 # the unit of time/t field in the PointCloud2 rostopic: 0-second, 1-milisecond, 2-microsecond, 3-nanosecond. Horizon_SCAN: 1800 # lidar horizontal resolution (Velodyne:1800, Ouster:512,1024,2048) blind: 2 mapping: acc_cov: 3.7686306102624571e-02 gyr_cov: 2.3417543020438883e-03 b_acc_cov: 1.1416642385952368e-03 b_gyr_cov: 1.4428407712885209e-05 fov_degree: 180 det_range: 100.0 extrinsic_est_en: false # true: enable the online estimation of IMU-LiDAR extrinsic, extrinsic_T: [0.27255, -0.00053, 0.17954] extrinsic_R: [ 1, 0, 0, 0, 1, 0, 0, 0, 1] #voxel map voxel_hash_en: false # for association voxel_size: 1.0 max_layer: 4 # 4 layer, 0, 1, 2, 3 layer_point_size: [ 5, 5, 5, 5, 5 ] # min points plane update threshold plannar_threshold: 0.01 max_points_size: 1000 max_cov_points_size: 1000 publish: path_en: true scan_publish_en: true # false: close all the point cloud output dense_publish_en: true # false: low down the points number in a global-frame point clouds scan. scan_bodyframe_pub_en: true # true: output the point cloud scans in IMU-body-frame pcd_save: pcd_save_en: false interval: -1 # how many LiDAR frames saved in each pcd file; # -1 : all frames will be saved in ONE pcd file, may lead to memory crash when having too much frames. save_ikdtree_map: false save_final_map: false normal: compute_table: false ring_table_dir: "/config/m2dgr" compute_normal: true check_normal: true surfel: surfel_points_min: 25 # points inside the voxel larger than the value, do pca surfel_points_max: 100 # max points inside a voxel planarity: 0.6 # larger than the value is plane [0, 1.0] mid2min: 100.0 # larger than the value is plane angle_threshold: 30.0 # angle between ring Fals and surfel normal cloud_surfel: true # point-to-neighbor surfel association point_surfel: true # point-to-point surfel association # prism or RTK in IMU frame ground_truth: extrinsic_T: [ 0.16, 0.0, 0.84 ] ## GNSS in IMU frame # extrinsic_T: [ 0.27255, -0.00053, 0.17954] ## LiDAR in IMU frame extrinsic_R: [ 1, 0, 0, 0, 1, 0, 0, 0, 1 ] ================================================ FILE: config/viral/ring16_M_0.xml ================================================ 16 1024
f
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0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. -9.58464861e-01 -4.47288975e-02 -2.81681657e-01 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. ================================================ FILE: config/viral.yaml ================================================ common: lid_topic: "/os1_cloud_node1/points" imu_topic: "/imu/imu" time_sync_en: false # ONLY turn on when external time synchronization is really not possible time_offset_lidar_to_imu: 0.0 # Time offset between lidar and IMU calibrated by other algorithms, e.g. LI-Init (can be found in README). # This param will take effect no matter what time_sync_en is. So if the time offset is not known exactly, please set as 0.0 lidar_time_offset: -0.10 # begin time = lidar timestamp + time offset preprocess: lidar_type: 3 # 1 for Livox serials LiDAR, 2 for Velodyne LiDAR, 3 for ouster LiDAR, scan_line: 16 scan_rate: 10 # only need to be set for velodyne, unit: Hz, timestamp_unit: 3 # 0-second, 1-milisecond, 2-microsecond, 3-nanosecond. Horizon_SCAN: 1024 # lidar horizontal resolution (Velodyne:1800, Ouster:512,1024,2048) blind: 2 mapping: acc_cov: 6.0e-2 gyr_cov: 5.0e-3 b_acc_cov: 8.0e-5 b_gyr_cov: 3.0e-6 fov_degree: 180 det_range: 150.0 extrinsic_est_en: false # true: enable the online estimation of IMU-LiDAR extrinsic extrinsic_T: [ -0.05, 0.0, 0.055 ] # imu <-- lidar extrinsic_R: [1, 0, 0, 0, 1, 0, 0, 0, 1] voxel_hash_en: true # for association publish: path_en: true scan_publish_en: true # false: close all the point cloud output dense_publish_en: true # false: low down the points number in a global-frame point clouds scan. scan_bodyframe_pub_en: true # true: output the point cloud scans in IMU-body-frame pcd_save: pcd_save_en: false interval: -1 # how many LiDAR frames saved in each pcd file; # -1 : all frames will be saved in ONE pcd file, may lead to memory crash when having too much frames. save_ikdtree_map: false save_final_map: false normal: compute_table: false ring_table_dir: "/config/viral" compute_normal: true check_normal: true surfel: surfel_points_min: 5 # points inside the voxel larger than the value, do pca surfel_points_max: 1000 # max points inside a voxel planarity: 0.6 # less than the value is plane [0, 1.0] mid2min: 100.0 # larger than the value is plane angle_threshold: 25.0 # angle between ring Fals and surfel normal cloud_surfel: true # point-to-neighbor surfel association point_surfel: true # point-to-point surfel association # prism in IMU frame ground_truth: extrinsic_T: [ -0.293656, -0.012288, -0.273095 ] extrinsic_R: [ 1, 0, 0, 0, 1, 0, 0, 0, 1 ] ================================================ FILE: include/Exp_mat.h ================================================ #ifndef EXP_MAT_H #define EXP_MAT_H #include #include #include // #include #define SKEW_SYM_MATRX(v) 0.0,-v[2],v[1],v[2],0.0,-v[0],-v[1],v[0],0.0 template Eigen::Matrix Exp(const Eigen::Matrix &&ang) { T ang_norm = ang.norm(); Eigen::Matrix Eye3 = Eigen::Matrix::Identity(); if (ang_norm > 0.0000001) { Eigen::Matrix r_axis = ang / ang_norm; Eigen::Matrix K; K << SKEW_SYM_MATRX(r_axis); /// Roderigous Tranformation return Eye3 + std::sin(ang_norm) * K + (1.0 - std::cos(ang_norm)) * K * K; } else { return Eye3; } } template Eigen::Matrix Exp(const Eigen::Matrix &ang_vel, const Ts &dt) { T ang_vel_norm = ang_vel.norm(); Eigen::Matrix Eye3 = Eigen::Matrix::Identity(); if (ang_vel_norm > 0.0000001) { Eigen::Matrix r_axis = ang_vel / ang_vel_norm; Eigen::Matrix K; K << SKEW_SYM_MATRX(r_axis); T r_ang = ang_vel_norm * dt; /// Roderigous Tranformation return Eye3 + std::sin(r_ang) * K + (1.0 - std::cos(r_ang)) * K * K; } else { return Eye3; } } template Eigen::Matrix Exp(const T &v1, const T &v2, const T &v3) { T &&norm = sqrt(v1 * v1 + v2 * v2 + v3 * v3); Eigen::Matrix Eye3 = Eigen::Matrix::Identity(); if (norm > 0.00001) { T r_ang[3] = {v1 / norm, v2 / norm, v3 / norm}; Eigen::Matrix K; K << SKEW_SYM_MATRX(r_ang); /// Roderigous Tranformation return Eye3 + std::sin(norm) * K + (1.0 - std::cos(norm)) * K * K; } else { return Eye3; } } /* Logrithm of a Rotation Matrix */ template Eigen::Matrix Log(const Eigen::Matrix &R) { T &&theta = std::acos(0.5 * (R.trace() - 1)); Eigen::Matrix K(R(2,1) - R(1,2), R(0,2) - R(2,0), R(1,0) - R(0,1)); return (std::abs(theta) < 0.001) ? (0.5 * K) : (0.5 * theta / std::sin(theta) * K); } // template // cv::Mat Exp(const T &v1, const T &v2, const T &v3) // { // T norm = sqrt(v1 * v1 + v2 * v2 + v3 * v3); // cv::Mat Eye3 = cv::Mat::eye(3, 3, CV_32F); // if (norm > 0.0000001) // { // T r_ang[3] = {v1 / norm, v2 / norm, v3 / norm}; // cv::Mat K = (cv::Mat_(3,3) << SKEW_SYM_MATRX(r_ang)); // /// Roderigous Tranformation // return Eye3 + std::sin(norm) * K + (1.0 - std::cos(norm)) * K * K; // } // else // { // return Eye3; // } // } #endif ================================================ FILE: include/IKFoM_toolkit/esekfom/esekfom.hpp ================================================ /* * Copyright (c) 2019--2023, The University of Hong Kong * All rights reserved. * * Author: Dongjiao HE * * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions * are met: * * * Redistributions of source code must retain the above copyright * notice, this list of conditions and the following disclaimer. * * Redistributions in binary form must reproduce the above * copyright notice, this list of conditions and the following * disclaimer in the documentation and/or other materials provided * with the distribution. * * Neither the name of the Universitaet Bremen nor the names of its * contributors may be used to endorse or promote products derived * from this software without specific prior written permission. * * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE * POSSIBILITY OF SUCH DAMAGE. */ #ifndef ESEKFOM_EKF_HPP #define ESEKFOM_EKF_HPP #include #include #include #include #include #include #include #include "../mtk/types/vect.hpp" #include "../mtk/types/SOn.hpp" #include "../mtk/types/S2.hpp" #include "../mtk/startIdx.hpp" #include "../mtk/build_manifold.hpp" #include "util.hpp" //#define USE_sparse namespace esekfom { using namespace Eigen; //used for iterated error state EKF update //for the aim to calculate measurement (z), estimate measurement (h), partial differention matrices (h_x, h_v) and the noise covariance (R) at the same time, by only one function. //applied for measurement as a manifold. template struct share_datastruct { bool valid; bool converge; M z; Eigen::Matrix h_v; Eigen::Matrix h_x; Eigen::Matrix R; }; //used for iterated error state EKF update //for the aim to calculate measurement (z), estimate measurement (h), partial differention matrices (h_x, h_v) and the noise covariance (R) at the same time, by only one function. //applied for measurement as an Eigen matrix whose dimension is changing template struct dyn_share_datastruct { bool valid; bool converge; Eigen::Matrix z; Eigen::Matrix h; // estimate measurement (h), distance Eigen::Matrix h_v; Eigen::Matrix h_x; // partial differention matrices, Jacobian Eigen::Matrix R; }; //used for iterated error state EKF update //for the aim to calculate measurement (z), estimate measurement (h), partial differention matrices (h_x, h_v) and the noise covariance (R) at the same time, by only one function. //applied for measurement as a dynamic manifold whose dimension or type is changing template struct dyn_runtime_share_datastruct { bool valid; bool converge; //Z z; Eigen::Matrix h_v; Eigen::Matrix h_x; Eigen::Matrix R; }; //状态量，噪声，输入状态量 template class esekf{ typedef esekf self; enum{ n = state::DOF, m = state::DIM, l = measurement::DOF }; public: typedef typename state::scalar scalar_type; typedef Matrix cov; typedef Matrix cov_; typedef SparseMatrix spMt; typedef Matrix vectorized_state; typedef Matrix flatted_state; typedef flatted_state processModel(state &, const input &); typedef Eigen::Matrix processMatrix1(state &, const input &); typedef Eigen::Matrix processMatrix2(state &, const input &); typedef Eigen::Matrix processnoisecovariance; typedef measurement measurementModel(state &, bool &); typedef measurement measurementModel_share(state &, share_datastruct &); typedef Eigen::Matrix measurementModel_dyn(state &, bool &); //typedef Eigen::Matrix measurementModel_dyn_share(state &, dyn_share_datastruct &); typedef void measurementModel_dyn_share(state &, dyn_share_datastruct &); typedef Eigen::Matrix measurementMatrix1(state &, bool&); typedef Eigen::Matrix measurementMatrix1_dyn(state &, bool&); typedef Eigen::Matrix measurementMatrix2(state &, bool&); typedef Eigen::Matrix measurementMatrix2_dyn(state &, bool&); typedef Eigen::Matrix measurementnoisecovariance; typedef Eigen::Matrix measurementnoisecovariance_dyn; esekf(const state &x = state(), const cov &P = cov::Identity()): x_(x), P_(P){ #ifdef USE_sparse SparseMatrix ref(n, n); ref.setIdentity(); l_ = ref; f_x_2 = ref; f_x_1 = ref; #endif }; //receive system-specific models and their differentions. //for measurement as a manifold. void init(processModel f_in, processMatrix1 f_x_in, processMatrix2 f_w_in, measurementModel h_in, measurementMatrix1 h_x_in, measurementMatrix2 h_v_in, int maximum_iteration, scalar_type limit_vector[n]) { f = f_in; f_x = f_x_in; f_w = f_w_in; h = h_in; h_x = h_x_in; h_v = h_v_in; maximum_iter = maximum_iteration; for(int i=0; i f_w_ = f_w(x_, i_in); Matrix f_w_final; state x_before = x_; x_.oplus(f_, dt); F_x1 = cov::Identity(); for (std::vector, int> >::iterator it = x_.vect_state.begin(); it != x_.vect_state.end(); it++) { int idx = (*it).first.first; int dim = (*it).first.second; int dof = (*it).second; for(int i = 0; i < n; i++){ for(int j=0; j res_temp_SO3; MTK::vect<3, scalar_type> seg_SO3; for (std::vector >::iterator it = x_.SO3_state.begin(); it != x_.SO3_state.end(); it++) { int idx = (*it).first; int dim = (*it).second; for(int i = 0; i < 3; i++){ seg_SO3(i) = -1 * f_(dim + i) * dt; } MTK::SO3 res; res.w() = MTK::exp(res.vec(), seg_SO3, scalar_type(1/2)); #ifdef USE_sparse res_temp_SO3 = res.toRotationMatrix(); for(int i = 0; i < 3; i++){ for(int j = 0; j < 3; j++){ f_x_1.coeffRef(idx + i, idx + j) = res_temp_SO3(i, j); } } #else F_x1.template block<3, 3>(idx, idx) = res.toRotationMatrix(); #endif res_temp_SO3 = MTK::A_matrix(seg_SO3); for(int i = 0; i < n; i++){ f_x_final. template block<3, 1>(idx, i) = res_temp_SO3 * (f_x_. template block<3, 1>(dim, i)); } for(int i = 0; i < process_noise_dof; i++){ f_w_final. template block<3, 1>(idx, i) = res_temp_SO3 * (f_w_. template block<3, 1>(dim, i)); } } Matrix res_temp_S2; Matrix res_temp_S2_; MTK::vect<3, scalar_type> seg_S2; for (std::vector >::iterator it = x_.S2_state.begin(); it != x_.S2_state.end(); it++) { int idx = (*it).first; int dim = (*it).second; for(int i = 0; i < 3; i++){ seg_S2(i) = f_(dim + i) * dt; } MTK::vect<2, scalar_type> vec = MTK::vect<2, scalar_type>::Zero(); MTK::SO3 res; res.w() = MTK::exp(res.vec(), seg_S2, scalar_type(1/2)); Eigen::Matrix Nx; Eigen::Matrix Mx; x_.S2_Nx_yy(Nx, idx); x_before.S2_Mx(Mx, vec, idx); #ifdef USE_sparse res_temp_S2_ = Nx * res.toRotationMatrix() * Mx; for(int i = 0; i < 2; i++){ for(int j = 0; j < 2; j++){ f_x_1.coeffRef(idx + i, idx + j) = res_temp_S2_(i, j); } } #else F_x1.template block<2, 2>(idx, idx) = Nx * res.toRotationMatrix() * Mx; #endif Eigen::Matrix x_before_hat; x_before.S2_hat(x_before_hat, idx); res_temp_S2 = -Nx * res.toRotationMatrix() * x_before_hat*MTK::A_matrix(seg_S2).transpose(); for(int i = 0; i < n; i++){ f_x_final. template block<2, 1>(idx, i) = res_temp_S2 * (f_x_. template block<3, 1>(dim, i)); } for(int i = 0; i < process_noise_dof; i++){ f_w_final. template block<2, 1>(idx, i) = res_temp_S2 * (f_w_. template block<3, 1>(dim, i)); } } // todo #ifdef USE_sparse f_x_1.makeCompressed(); spMt f_x2 = f_x_final.sparseView(); spMt f_w1 = f_w_final.sparseView(); spMt xp = f_x_1 + f_x2 * dt; P_ = xp * P_ * xp.transpose() + (f_w1 * dt) * Q * (f_w1 * dt).transpose(); #else F_x1 += f_x_final * dt; P_ = (F_x1) * P_ * (F_x1).transpose() + (dt * f_w_final) * Q * (dt * f_w_final).transpose();//更新协方差矩阵 #endif } //iterated error state EKF update for measurement as a manifold. void update_iterated(measurement& z, measurementnoisecovariance &R) { if(!(is_same())){ std::cerr << "the scalar type of measurment must be the same as the state" << std::endl; std::exit(100); } int t = 0; bool converg = true; bool valid = true; state x_propagated = x_; cov P_propagated = P_; for(int i=-1; i h_x_ = h_x(x_, valid); Matrix h_v_ = h_v(x_, valid); #endif if(! valid) { continue; } P_ = P_propagated; Matrix res_temp_SO3; MTK::vect<3, scalar_type> seg_SO3; for (std::vector >::iterator it = x_.SO3_state.begin(); it != x_.SO3_state.end(); it++) { int idx = (*it).first; int dim = (*it).second; for(int i = 0; i < 3; i++){ seg_SO3(i) = dx(idx+i); } res_temp_SO3 = A_matrix(seg_SO3).transpose(); dx_new.template block<3, 1>(idx, 0) = res_temp_SO3 * dx.template block<3, 1>(idx, 0); for(int i = 0; i < n; i++){ P_. template block<3, 1>(idx, i) = res_temp_SO3 * (P_. template block<3, 1>(idx, i)); } for(int i = 0; i < n; i++){ P_. template block<1, 3>(i, idx) =(P_. template block<1, 3>(i, idx)) * res_temp_SO3.transpose(); } } Matrix res_temp_S2; MTK::vect<2, scalar_type> seg_S2; for (std::vector >::iterator it = x_.S2_state.begin(); it != x_.S2_state.end(); it++) { int idx = (*it).first; int dim = (*it).second; for(int i = 0; i < 2; i++){ seg_S2(i) = dx(idx + i); } Eigen::Matrix Nx; Eigen::Matrix Mx; x_.S2_Nx_yy(Nx, idx); x_propagated.S2_Mx(Mx, seg_S2, idx); res_temp_S2 = Nx * Mx; dx_new.template block<2, 1>(idx, 0) = res_temp_S2 * dx.template block<2, 1>(idx, 0); for(int i = 0; i < n; i++){ P_. template block<2, 1>(idx, i) = res_temp_S2 * (P_. template block<2, 1>(idx, i)); } for(int i = 0; i < n; i++){ P_. template block<1, 2>(i, idx) = (P_. template block<1, 2>(i, idx)) * res_temp_S2.transpose(); } } Matrix K_; if(n > l) { #ifdef USE_sparse Matrix K_temp = h_x_ * P_ * h_x_.transpose(); spMt R_temp = h_v_ * R_ * h_v_.transpose(); K_temp += R_temp; K_ = P_ * h_x_.transpose() * K_temp.inverse(); #else K_= P_ * h_x_.transpose() * (h_x_ * P_ * h_x_.transpose() + h_v_ * R * h_v_.transpose()).inverse(); #endif } else { #ifdef USE_sparse measurementnoisecovariance b = measurementnoisecovariance::Identity(); Eigen::SparseQR, Eigen::COLAMDOrdering> solver; solver.compute(R_); measurementnoisecovariance R_in_temp = solver.solve(b); spMt R_in = R_in_temp.sparseView(); spMt K_temp = h_x_.transpose() * R_in * h_x_; cov P_temp = P_.inverse(); P_temp += K_temp; K_ = P_temp.inverse() * h_x_.transpose() * R_in; #else measurementnoisecovariance R_in = (h_v_*R*h_v_.transpose()).inverse(); K_ = (h_x_.transpose() * R_in * h_x_ + P_.inverse()).inverse() * h_x_.transpose() * R_in; #endif } Matrix innovation; z.boxminus(innovation, h(x_, valid)); cov K_x = K_ * h_x_; Matrix dx_ = K_ * innovation + (K_x - Matrix::Identity()) * dx_new; state x_before = x_; x_.boxplus(dx_); converg = true; for(int i = 0; i < n ; i++) { if(std::fabs(dx_[i]) > limit[i]) { converg = false; break; } } if(converg) t++; if(t > 1 || i == maximum_iter - 1) { L_ = P_; Matrix res_temp_SO3; MTK::vect<3, scalar_type> seg_SO3; for(typename std::vector >::iterator it = x_.SO3_state.begin(); it != x_.SO3_state.end(); it++) { int idx = (*it).first; for(int i = 0; i < 3; i++){ seg_SO3(i) = dx_(i + idx); } res_temp_SO3 = A_matrix(seg_SO3).transpose(); for(int i = 0; i < n; i++){ L_. template block<3, 1>(idx, i) = res_temp_SO3 * (P_. template block<3, 1>(idx, i)); } if(n > l) { for(int i = 0; i < l; i++){ K_. template block<3, 1>(idx, i) = res_temp_SO3 * (K_. template block<3, 1>(idx, i)); } } else { for(int i = 0; i < n; i++){ K_x. template block<3, 1>(idx, i) = res_temp_SO3 * (K_x. template block<3, 1>(idx, i)); } } for(int i = 0; i < n; i++){ L_. template block<1, 3>(i, idx) = (L_. template block<1, 3>(i, idx)) * res_temp_SO3.transpose(); P_. template block<1, 3>(i, idx) = (P_. template block<1, 3>(i, idx)) * res_temp_SO3.transpose(); } } Matrix res_temp_S2; MTK::vect<2, scalar_type> seg_S2; for(typename std::vector >::iterator it = x_.S2_state.begin(); it != x_.S2_state.end(); it++) { int idx = (*it).first; for(int i = 0; i < 2; i++){ seg_S2(i) = dx_(i + idx); } Eigen::Matrix Nx; Eigen::Matrix Mx; x_.S2_Nx_yy(Nx, idx); x_propagated.S2_Mx(Mx, seg_S2, idx); res_temp_S2 = Nx * Mx; for(int i = 0; i < n; i++){ L_. template block<2, 1>(idx, i) = res_temp_S2 * (P_. template block<2, 1>(idx, i)); } if(n > l) { for(int i = 0; i < l; i++){ K_. template block<2, 1>(idx, i) = res_temp_S2 * (K_. template block<2, 1>(idx, i)); } } else { for(int i = 0; i < n; i++){ K_x. template block<2, 1>(idx, i) = res_temp_S2 * (K_x. template block<2, 1>(idx, i)); } } for(int i = 0; i < n; i++){ L_. template block<1, 2>(i, idx) = (L_. template block<1, 2>(i, idx)) * res_temp_S2.transpose(); P_. template block<1, 2>(i, idx) = (P_. template block<1, 2>(i, idx)) * res_temp_S2.transpose(); } } if(n > l) { P_ = L_ - K_ * h_x_ * P_; } else { P_ = L_ - K_x * P_; } return; } } } //iterated error state EKF update for measurement as a manifold. //calculate measurement (z), estimate measurement (h), partial differention matrices (h_x, h_v) and the noise covariance (R) at the same time, by only one function. void update_iterated_share() { if(!(is_same())){ std::cerr << "the scalar type of measurment must be the same as the state" << std::endl; std::exit(100); } int t = 0; share_datastruct _share; _share.valid = true; _share.converge = true; state x_propagated = x_; cov P_propagated = P_; for(int i=-1; i h_x_ = _share.h_x; Matrix h_v_ = _share.h_v; #endif if(! _share.valid) { continue; } P_ = P_propagated; Matrix res_temp_SO3; MTK::vect<3, scalar_type> seg_SO3; for (std::vector >::iterator it = x_.SO3_state.begin(); it != x_.SO3_state.end(); it++) { int idx = (*it).first; int dim = (*it).second; for(int i = 0; i < 3; i++){ seg_SO3(i) = dx(idx+i); } res_temp_SO3 = A_matrix(seg_SO3).transpose(); dx_new.template block<3, 1>(idx, 0) = res_temp_SO3 * dx.template block<3, 1>(idx, 0); for(int i = 0; i < n; i++){ P_. template block<3, 1>(idx, i) = res_temp_SO3 * (P_. template block<3, 1>(idx, i)); } for(int i = 0; i < n; i++){ P_. template block<1, 3>(i, idx) =(P_. template block<1, 3>(i, idx)) * res_temp_SO3.transpose(); } } Matrix res_temp_S2; MTK::vect<2, scalar_type> seg_S2; for (std::vector >::iterator it = x_.S2_state.begin(); it != x_.S2_state.end(); it++) { int idx = (*it).first; int dim = (*it).second; for(int i = 0; i < 2; i++){ seg_S2(i) = dx(idx + i); } Eigen::Matrix Nx; Eigen::Matrix Mx; x_.S2_Nx_yy(Nx, idx); x_propagated.S2_Mx(Mx, seg_S2, idx); res_temp_S2 = Nx * Mx; dx_new.template block<2, 1>(idx, 0) = res_temp_S2 * dx.template block<2, 1>(idx, 0); for(int i = 0; i < n; i++){ P_. template block<2, 1>(idx, i) = res_temp_S2 * (P_. template block<2, 1>(idx, i)); } for(int i = 0; i < n; i++){ P_. template block<1, 2>(i, idx) = (P_. template block<1, 2>(i, idx)) * res_temp_S2.transpose(); } } Matrix K_; if(n > l) { #ifdef USE_sparse Matrix K_temp = h_x_ * P_ * h_x_.transpose(); spMt R_temp = h_v_ * R_ * h_v_.transpose(); K_temp += R_temp; K_ = P_ * h_x_.transpose() * K_temp.inverse(); #else K_= P_ * h_x_.transpose() * (h_x_ * P_ * h_x_.transpose() + h_v_ * R * h_v_.transpose()).inverse(); #endif } else { #ifdef USE_sparse measurementnoisecovariance b = measurementnoisecovariance::Identity(); Eigen::SparseQR, Eigen::COLAMDOrdering> solver; solver.compute(R_); measurementnoisecovariance R_in_temp = solver.solve(b); spMt R_in = R_in_temp.sparseView(); spMt K_temp = h_x_.transpose() * R_in * h_x_; cov P_temp = P_.inverse(); P_temp += K_temp; K_ = P_temp.inverse() * h_x_.transpose() * R_in; #else measurementnoisecovariance R_in = (h_v_*R*h_v_.transpose()).inverse(); K_ = (h_x_.transpose() * R_in * h_x_ + P_.inverse()).inverse() * h_x_.transpose() * R_in; #endif } Matrix innovation; z.boxminus(innovation, h); cov K_x = K_ * h_x_; Matrix dx_ = K_ * innovation + (K_x - Matrix::Identity()) * dx_new; state x_before = x_; x_.boxplus(dx_); _share.converge = true; for(int i = 0; i < n ; i++) { if(std::fabs(dx_[i]) > limit[i]) { _share.converge = false; break; } } if(_share.converge) t++; if(t > 1 || i == maximum_iter - 1) { L_ = P_; Matrix res_temp_SO3; MTK::vect<3, scalar_type> seg_SO3; for(typename std::vector >::iterator it = x_.SO3_state.begin(); it != x_.SO3_state.end(); it++) { int idx = (*it).first; for(int i = 0; i < 3; i++){ seg_SO3(i) = dx_(i + idx); } res_temp_SO3 = A_matrix(seg_SO3).transpose(); for(int i = 0; i < n; i++){ L_. template block<3, 1>(idx, i) = res_temp_SO3 * (P_. template block<3, 1>(idx, i)); } if(n > l) { for(int i = 0; i < l; i++){ K_. template block<3, 1>(idx, i) = res_temp_SO3 * (K_. template block<3, 1>(idx, i)); } } else { for(int i = 0; i < n; i++){ K_x. template block<3, 1>(idx, i) = res_temp_SO3 * (K_x. template block<3, 1>(idx, i)); } } for(int i = 0; i < n; i++){ L_. template block<1, 3>(i, idx) = (L_. template block<1, 3>(i, idx)) * res_temp_SO3.transpose(); P_. template block<1, 3>(i, idx) = (P_. template block<1, 3>(i, idx)) * res_temp_SO3.transpose(); } } Matrix res_temp_S2; MTK::vect<2, scalar_type> seg_S2; for(typename std::vector >::iterator it = x_.S2_state.begin(); it != x_.S2_state.end(); it++) { int idx = (*it).first; for(int i = 0; i < 2; i++){ seg_S2(i) = dx_(i + idx); } Eigen::Matrix Nx; Eigen::Matrix Mx; x_.S2_Nx_yy(Nx, idx); x_propagated.S2_Mx(Mx, seg_S2, idx); res_temp_S2 = Nx * Mx; for(int i = 0; i < n; i++){ L_. template block<2, 1>(idx, i) = res_temp_S2 * (P_. template block<2, 1>(idx, i)); } if(n > l) { for(int i = 0; i < l; i++){ K_. template block<2, 1>(idx, i) = res_temp_S2 * (K_. template block<2, 1>(idx, i)); } } else { for(int i = 0; i < n; i++){ K_x. template block<2, 1>(idx, i) = res_temp_S2 * (K_x. template block<2, 1>(idx, i)); } } for(int i = 0; i < n; i++){ L_. template block<1, 2>(i, idx) = (L_. template block<1, 2>(i, idx)) * res_temp_S2.transpose(); P_. template block<1, 2>(i, idx) = (P_. template block<1, 2>(i, idx)) * res_temp_S2.transpose(); } } if(n > l) { P_ = L_ - K_ * h_x_ * P_; } else { P_ = L_ - K_x * P_; } return; } } } //iterated error state EKF update for measurement as an Eigen matrix whose dimension is changing. void update_iterated_dyn(Eigen::Matrix z, measurementnoisecovariance_dyn R) { int t = 0; bool valid = true; bool converg = true; state x_propagated = x_; cov P_propagated = P_; int dof_Measurement; int dof_Measurement_noise = R.rows(); for(int i=-1; i h_x_ = h_x_dyn(x_, valid); Matrix h_v_ = h_v_dyn(x_, valid); #endif Matrix h_ = h_dyn(x_, valid); dof_Measurement = h_.rows(); vectorized_state dx, dx_new; x_.boxminus(dx, x_propagated); dx_new = dx; if(! valid) { continue; } P_ = P_propagated; Matrix res_temp_SO3; MTK::vect<3, scalar_type> seg_SO3; for (std::vector >::iterator it = x_.SO3_state.begin(); it != x_.SO3_state.end(); it++) { int idx = (*it).first; int dim = (*it).second; for(int i = 0; i < 3; i++){ seg_SO3(i) = dx(idx+i); } res_temp_SO3 = MTK::A_matrix(seg_SO3).transpose(); dx_new.template block<3, 1>(idx, 0) = res_temp_SO3 * dx_new.template block<3, 1>(idx, 0); for(int i = 0; i < n; i++){ P_. template block<3, 1>(idx, i) = res_temp_SO3 * (P_. template block<3, 1>(idx, i)); } for(int i = 0; i < n; i++){ P_. template block<1, 3>(i, idx) =(P_. template block<1, 3>(i, idx)) * res_temp_SO3.transpose(); } } Matrix res_temp_S2; MTK::vect<2, scalar_type> seg_S2; for (std::vector >::iterator it = x_.S2_state.begin(); it != x_.S2_state.end(); it++) { int idx = (*it).first; int dim = (*it).second; for(int i = 0; i < 2; i++){ seg_S2(i) = dx(idx + i); } Eigen::Matrix Nx; Eigen::Matrix Mx; x_.S2_Nx_yy(Nx, idx); x_propagated.S2_Mx(Mx, seg_S2, idx); res_temp_S2 = Nx * Mx; dx_new.template block<2, 1>(idx, 0) = res_temp_S2 * dx_new.template block<2, 1>(idx, 0); for(int i = 0; i < n; i++){ P_. template block<2, 1>(idx, i) = res_temp_S2 * (P_. template block<2, 1>(idx, i)); } for(int i = 0; i < n; i++){ P_. template block<1, 2>(i, idx) = (P_. template block<1, 2>(i, idx)) * res_temp_S2.transpose(); } } Matrix K_; if(n > dof_Measurement) { #ifdef USE_sparse Matrix K_temp = h_x_ * P_ * h_x_.transpose(); spMt R_temp = h_v_ * R_ * h_v_.transpose(); K_temp += R_temp; K_ = P_ * h_x_.transpose() * K_temp.inverse(); #else K_= P_ * h_x_.transpose() * (h_x_ * P_ * h_x_.transpose() + h_v_ * R * h_v_.transpose()).inverse(); #endif } else { #ifdef USE_sparse Eigen::Matrix b = Eigen::Matrix::Identity(dof_Measurement_noise, dof_Measurement_noise); Eigen::SparseQR, Eigen::COLAMDOrdering> solver; solver.compute(R_); Eigen::Matrix R_in_temp = solver.solve(b); spMt R_in = R_in_temp.sparseView(); spMt K_temp = h_x_.transpose() * R_in * h_x_; cov P_temp = P_.inverse(); P_temp += K_temp; K_ = P_temp.inverse() * h_x_.transpose() * R_in; #else Eigen::Matrix R_in = (h_v_*R*h_v_.transpose()).inverse(); K_ = (h_x_.transpose() * R_in * h_x_ + P_.inverse()).inverse() * h_x_.transpose() * R_in; #endif } cov K_x = K_ * h_x_; Matrix dx_ = K_ * (z - h_) + (K_x - Matrix::Identity()) * dx_new; state x_before = x_; x_.boxplus(dx_); converg = true; for(int i = 0; i < n ; i++) { if(std::fabs(dx_[i]) > limit[i]) { converg = false; break; } } if(converg) t++; if(t > 1 || i == maximum_iter - 1) { L_ = P_; std::cout << "iteration time:" << t << "," << i << std::endl; Matrix res_temp_SO3; MTK::vect<3, scalar_type> seg_SO3; for(typename std::vector >::iterator it = x_.SO3_state.begin(); it != x_.SO3_state.end(); it++) { int idx = (*it).first; for(int i = 0; i < 3; i++){ seg_SO3(i) = dx_(i + idx); } res_temp_SO3 = MTK::A_matrix(seg_SO3).transpose(); for(int i = 0; i < n; i++){ L_. template block<3, 1>(idx, i) = res_temp_SO3 * (P_. template block<3, 1>(idx, i)); } if(n > dof_Measurement) { for(int i = 0; i < dof_Measurement; i++){ K_. template block<3, 1>(idx, i) = res_temp_SO3 * (K_. template block<3, 1>(idx, i)); } } else { for(int i = 0; i < n; i++){ K_x. template block<3, 1>(idx, i) = res_temp_SO3 * (K_x. template block<3, 1>(idx, i)); } } for(int i = 0; i < n; i++){ L_. template block<1, 3>(i, idx) = (L_. template block<1, 3>(i, idx)) * res_temp_SO3.transpose(); P_. template block<1, 3>(i, idx) = (P_. template block<1, 3>(i, idx)) * res_temp_SO3.transpose(); } } Matrix res_temp_S2; MTK::vect<2, scalar_type> seg_S2; for(typename std::vector >::iterator it = x_.S2_state.begin(); it != x_.S2_state.end(); it++) { int idx = (*it).first; for(int i = 0; i < 2; i++){ seg_S2(i) = dx_(i + idx); } Eigen::Matrix Nx; Eigen::Matrix Mx; x_.S2_Nx_yy(Nx, idx); x_propagated.S2_Mx(Mx, seg_S2, idx); res_temp_S2 = Nx * Mx; for(int i = 0; i < n; i++){ L_. template block<2, 1>(idx, i) = res_temp_S2 * (P_. template block<2, 1>(idx, i)); } if(n > dof_Measurement) { for(int i = 0; i < dof_Measurement; i++){ K_. template block<2, 1>(idx, i) = res_temp_S2 * (K_. template block<2, 1>(idx, i)); } } else { for(int i = 0; i < n; i++){ K_x. template block<2, 1>(idx, i) = res_temp_S2 * (K_x. template block<2, 1>(idx, i)); } } for(int i = 0; i < n; i++){ L_. template block<1, 2>(i, idx) = (L_. template block<1, 2>(i, idx)) * res_temp_S2.transpose(); P_. template block<1, 2>(i, idx) = (P_. template block<1, 2>(i, idx)) * res_temp_S2.transpose(); } } if(n > dof_Measurement) { P_ = L_ - K_*h_x_*P_; } else { P_ = L_ - K_x * P_; } return; } } } //iterated error state EKF update for measurement as an Eigen matrix whose dimension is changing. //calculate measurement (z), estimate measurement (h), partial differention matrices (h_x, h_v) and the noise covariance (R) at the same time, by only one function. void update_iterated_dyn_share() { int t = 0; dyn_share_datastruct dyn_share; dyn_share.valid = true; dyn_share.converge = true; state x_propagated = x_; cov P_propagated = P_; int dof_Measurement; int dof_Measurement_noise; for(int i=-1; i h = h_dyn_share (x_, dyn_share); Matrix z = dyn_share.z; Matrix h = dyn_share.h; #ifdef USE_sparse spMt h_x = dyn_share.h_x.sparseView(); spMt h_v = dyn_share.h_v.sparseView(); spMt R_ = dyn_share.R.sparseView(); #else Matrix R = dyn_share.R; Matrix h_x = dyn_share.h_x; Matrix h_v = dyn_share.h_v; #endif dof_Measurement = h_x.rows(); dof_Measurement_noise = dyn_share.R.rows(); vectorized_state dx, dx_new; x_.boxminus(dx, x_propagated); dx_new = dx; if(! (dyn_share.valid)) { continue; } P_ = P_propagated; Matrix res_temp_SO3; MTK::vect<3, scalar_type> seg_SO3; for (std::vector >::iterator it = x_.SO3_state.begin(); it != x_.SO3_state.end(); it++) { int idx = (*it).first; int dim = (*it).second; for(int i = 0; i < 3; i++){ seg_SO3(i) = dx(idx+i); } res_temp_SO3 = MTK::A_matrix(seg_SO3).transpose(); dx_new.template block<3, 1>(idx, 0) = res_temp_SO3 * dx_new.template block<3, 1>(idx, 0); for(int i = 0; i < n; i++){ P_. template block<3, 1>(idx, i) = res_temp_SO3 * (P_. template block<3, 1>(idx, i)); } for(int i = 0; i < n; i++){ P_. template block<1, 3>(i, idx) =(P_. template block<1, 3>(i, idx)) * res_temp_SO3.transpose(); } } Matrix res_temp_S2; MTK::vect<2, scalar_type> seg_S2; for (std::vector >::iterator it = x_.S2_state.begin(); it != x_.S2_state.end(); it++) { int idx = (*it).first; int dim = (*it).second; for(int i = 0; i < 2; i++){ seg_S2(i) = dx(idx + i); } Eigen::Matrix Nx; Eigen::Matrix Mx; x_.S2_Nx_yy(Nx, idx); x_propagated.S2_Mx(Mx, seg_S2, idx); res_temp_S2 = Nx * Mx; dx_new.template block<2, 1>(idx, 0) = res_temp_S2 * dx_new.template block<2, 1>(idx, 0); for(int i = 0; i < n; i++){ P_. template block<2, 1>(idx, i) = res_temp_S2 * (P_. template block<2, 1>(idx, i)); } for(int i = 0; i < n; i++){ P_. template block<1, 2>(i, idx) = (P_. template block<1, 2>(i, idx)) * res_temp_S2.transpose(); } } Matrix K_; if(n > dof_Measurement) { #ifdef USE_sparse Matrix K_temp = h_x * P_ * h_x.transpose(); spMt R_temp = h_v * R_ * h_v.transpose(); K_temp += R_temp; K_ = P_ * h_x.transpose() * K_temp.inverse(); #else K_= P_ * h_x.transpose() * (h_x * P_ * h_x.transpose() + h_v * R * h_v.transpose()).inverse(); #endif } else { #ifdef USE_sparse Eigen::Matrix b = Eigen::Matrix::Identity(dof_Measurement_noise, dof_Measurement_noise); Eigen::SparseQR, Eigen::COLAMDOrdering> solver; solver.compute(R_); Eigen::Matrix R_in_temp = solver.solve(b); spMt R_in = R_in_temp.sparseView(); spMt K_temp = h_x.transpose() * R_in * h_x; cov P_temp = P_.inverse(); P_temp += K_temp; K_ = P_temp.inverse() * h_x.transpose() * R_in; #else Eigen::Matrix R_in = (h_v*R*h_v.transpose()).inverse(); K_ = (h_x.transpose() * R_in * h_x + P_.inverse()).inverse() * h_x.transpose() * R_in; #endif } cov K_x = K_ * h_x; Matrix dx_ = K_ * (z - h) + (K_x - Matrix::Identity()) * dx_new; state x_before = x_; x_.boxplus(dx_); dyn_share.converge = true; for(int i = 0; i < n ; i++) { if(std::fabs(dx_[i]) > limit[i]) { dyn_share.converge = false; break; } } if(dyn_share.converge) t++; if(t > 1 || i == maximum_iter - 1) { L_ = P_; std::cout << "iteration time:" << t << "," << i << std::endl; Matrix res_temp_SO3; MTK::vect<3, scalar_type> seg_SO3; for(typename std::vector >::iterator it = x_.SO3_state.begin(); it != x_.SO3_state.end(); it++) { int idx = (*it).first; for(int i = 0; i < 3; i++){ seg_SO3(i) = dx_(i + idx); } res_temp_SO3 = MTK::A_matrix(seg_SO3).transpose(); for(int i = 0; i < int(n); i++){ L_. template block<3, 1>(idx, i) = res_temp_SO3 * (P_. template block<3, 1>(idx, i)); } if(n > dof_Measurement) { for(int i = 0; i < dof_Measurement; i++){ K_. template block<3, 1>(idx, i) = res_temp_SO3 * (K_. template block<3, 1>(idx, i)); } } else { for(int i = 0; i < n; i++){ K_x. template block<3, 1>(idx, i) = res_temp_SO3 * (K_x. template block<3, 1>(idx, i)); } } for(int i = 0; i < n; i++){ L_. template block<1, 3>(i, idx) = (L_. template block<1, 3>(i, idx)) * res_temp_SO3.transpose(); P_. template block<1, 3>(i, idx) = (P_. template block<1, 3>(i, idx)) * res_temp_SO3.transpose(); } } Matrix res_temp_S2; MTK::vect<2, scalar_type> seg_S2; for(typename std::vector >::iterator it = x_.S2_state.begin(); it != x_.S2_state.end(); it++) { int idx = (*it).first; for(int i = 0; i < 2; i++){ seg_S2(i) = dx_(i + idx); } Eigen::Matrix Nx; Eigen::Matrix Mx; x_.S2_Nx_yy(Nx, idx); x_propagated.S2_Mx(Mx, seg_S2, idx); res_temp_S2 = Nx * Mx; for(int i = 0; i < n; i++){ L_. template block<2, 1>(idx, i) = res_temp_S2 * (P_. template block<2, 1>(idx, i)); } if(n > dof_Measurement) { for(int i = 0; i < dof_Measurement; i++){ K_. template block<2, 1>(idx, i) = res_temp_S2 * (K_. template block<2, 1>(idx, i)); } } else { for(int i = 0; i < n; i++){ K_x. template block<2, 1>(idx, i) = res_temp_S2 * (K_x. template block<2, 1>(idx, i)); } } for(int i = 0; i < n; i++){ L_. template block<1, 2>(i, idx) = (L_. template block<1, 2>(i, idx)) * res_temp_S2.transpose(); P_. template block<1, 2>(i, idx) = (P_. template block<1, 2>(i, idx)) * res_temp_S2.transpose(); } } if(n > dof_Measurement) { P_ = L_ - K_*h_x*P_; } else { P_ = L_ - K_x * P_; } return; } } } //iterated error state EKF update for measurement as a dynamic manifold, whose dimension or type is changing. //the measurement and the measurement model are received in a dynamic manner. template void update_iterated_dyn_runtime(measurement_runtime z, measurementnoisecovariance_dyn R, measurementModel_runtime h_runtime) { int t = 0; bool valid = true; bool converg = true; state x_propagated = x_; cov P_propagated = P_; int dof_Measurement; int dof_Measurement_noise; for(int i=-1; i h_x_ = h_x_dyn(x_, valid); Matrix h_v_ = h_v_dyn(x_, valid); #endif measurement_runtime h_ = h_runtime(x_, valid); dof_Measurement = measurement_runtime::DOF; dof_Measurement_noise = R.rows(); vectorized_state dx, dx_new; x_.boxminus(dx, x_propagated); dx_new = dx; if(! valid) { continue; } P_ = P_propagated; Matrix res_temp_SO3; MTK::vect<3, scalar_type> seg_SO3; for (std::vector >::iterator it = x_.SO3_state.begin(); it != x_.SO3_state.end(); it++) { int idx = (*it).first; int dim = (*it).second; for(int i = 0; i < 3; i++){ seg_SO3(i) = dx(idx+i); } res_temp_SO3 = MTK::A_matrix(seg_SO3).transpose(); dx_new.template block<3, 1>(idx, 0) = res_temp_SO3 * dx_new.template block<3, 1>(idx, 0); for(int i = 0; i < n; i++){ P_. template block<3, 1>(idx, i) = res_temp_SO3 * (P_. template block<3, 1>(idx, i)); } for(int i = 0; i < n; i++){ P_. template block<1, 3>(i, idx) =(P_. template block<1, 3>(i, idx)) * res_temp_SO3.transpose(); } } Matrix res_temp_S2; MTK::vect<2, scalar_type> seg_S2; for (std::vector >::iterator it = x_.S2_state.begin(); it != x_.S2_state.end(); it++) { int idx = (*it).first; int dim = (*it).second; for(int i = 0; i < 2; i++){ seg_S2(i) = dx(idx + i); } Eigen::Matrix Nx; Eigen::Matrix Mx; x_.S2_Nx_yy(Nx, idx); x_propagated.S2_Mx(Mx, seg_S2, idx); res_temp_S2 = Nx * Mx; dx_new.template block<2, 1>(idx, 0) = res_temp_S2 * dx_new.template block<2, 1>(idx, 0); for(int i = 0; i < n; i++){ P_. template block<2, 1>(idx, i) = res_temp_S2 * (P_. template block<2, 1>(idx, i)); } for(int i = 0; i < n; i++){ P_. template block<1, 2>(i, idx) = (P_. template block<1, 2>(i, idx)) * res_temp_S2.transpose(); } } Matrix K_; if(n > dof_Measurement) { #ifdef USE_sparse Matrix K_temp = h_x_ * P_ * h_x_.transpose(); spMt R_temp = h_v_ * R_ * h_v_.transpose(); K_temp += R_temp; K_ = P_ * h_x_.transpose() * K_temp.inverse(); #else K_= P_ * h_x_.transpose() * (h_x_ * P_ * h_x_.transpose() + h_v_ * R * h_v_.transpose()).inverse(); #endif } else { #ifdef USE_sparse Eigen::Matrix b = Eigen::Matrix::Identity(dof_Measurement_noise, dof_Measurement_noise); Eigen::SparseQR, Eigen::COLAMDOrdering> solver; solver.compute(R_); Eigen::Matrix R_in_temp = solver.solve(b); spMt R_in = R_in_temp.sparseView(); spMt K_temp = h_x_.transpose() * R_in * h_x_; cov P_temp = P_.inverse(); P_temp += K_temp; K_ = P_temp.inverse() * h_x_.transpose() * R_in; #else Eigen::Matrix R_in = (h_v_*R*h_v_.transpose()).inverse(); K_ = (h_x_.transpose() * R_in * h_x_ + P_.inverse()).inverse() * h_x_.transpose() * R_in; #endif } cov K_x = K_ * h_x_; Eigen::Matrix innovation; z.boxminus(innovation, h_); Matrix dx_ = K_ * innovation + (K_x - Matrix::Identity()) * dx_new; state x_before = x_; x_.boxplus(dx_); converg = true; for(int i = 0; i < n ; i++) { if(std::fabs(dx_[i]) > limit[i]) { converg = false; break; } } if(converg) t++; if(t > 1 || i == maximum_iter - 1) { L_ = P_; std::cout << "iteration time:" << t << "," << i << std::endl; Matrix res_temp_SO3; MTK::vect<3, scalar_type> seg_SO3; for(typename std::vector >::iterator it = x_.SO3_state.begin(); it != x_.SO3_state.end(); it++) { int idx = (*it).first; for(int i = 0; i < 3; i++){ seg_SO3(i) = dx_(i + idx); } res_temp_SO3 = MTK::A_matrix(seg_SO3).transpose(); for(int i = 0; i < n; i++){ L_. template block<3, 1>(idx, i) = res_temp_SO3 * (P_. template block<3, 1>(idx, i)); } if(n > dof_Measurement) { for(int i = 0; i < dof_Measurement; i++){ K_. template block<3, 1>(idx, i) = res_temp_SO3 * (K_. template block<3, 1>(idx, i)); } } else { for(int i = 0; i < n; i++){ K_x. template block<3, 1>(idx, i) = res_temp_SO3 * (K_x. template block<3, 1>(idx, i)); } } for(int i = 0; i < n; i++){ L_. template block<1, 3>(i, idx) = (L_. template block<1, 3>(i, idx)) * res_temp_SO3.transpose(); P_. template block<1, 3>(i, idx) = (P_. template block<1, 3>(i, idx)) * res_temp_SO3.transpose(); } } Matrix res_temp_S2; MTK::vect<2, scalar_type> seg_S2; for(typename std::vector >::iterator it = x_.S2_state.begin(); it != x_.S2_state.end(); it++) { int idx = (*it).first; for(int i = 0; i < 2; i++){ seg_S2(i) = dx_(i + idx); } Eigen::Matrix Nx; Eigen::Matrix Mx; x_.S2_Nx_yy(Nx, idx); x_propagated.S2_Mx(Mx, seg_S2, idx); res_temp_S2 = Nx * Mx; for(int i = 0; i < n; i++){ L_. template block<2, 1>(idx, i) = res_temp_S2 * (P_. template block<2, 1>(idx, i)); } if(n > dof_Measurement) { for(int i = 0; i < dof_Measurement; i++){ K_. template block<2, 1>(idx, i) = res_temp_S2 * (K_. template block<2, 1>(idx, i)); } } else { for(int i = 0; i < n; i++){ K_x. template block<2, 1>(idx, i) = res_temp_S2 * (K_x. template block<2, 1>(idx, i)); } } for(int i = 0; i < n; i++){ L_. template block<1, 2>(i, idx) = (L_. template block<1, 2>(i, idx)) * res_temp_S2.transpose(); P_. template block<1, 2>(i, idx) = (P_. template block<1, 2>(i, idx)) * res_temp_S2.transpose(); } } if(n > dof_Measurement) { P_ = L_ - K_*h_x_*P_; } else { P_ = L_ - K_x * P_; } return; } } } //iterated error state EKF update for measurement as a dynamic manifold, whose dimension or type is changing. //the measurement and the measurement model are received in a dynamic manner. //calculate measurement (z), estimate measurement (h), partial differention matrices (h_x, h_v) and the noise covariance (R) at the same time, by only one function. template void update_iterated_dyn_runtime_share(measurement_runtime z, measurementModel_dyn_runtime_share h) { int t = 0; dyn_runtime_share_datastruct dyn_share; dyn_share.valid = true; dyn_share.converge = true; state x_propagated = x_; cov P_propagated = P_; int dof_Measurement; int dof_Measurement_noise; for(int i=-1; i R = dyn_share.R; Matrix h_x = dyn_share.h_x; Matrix h_v = dyn_share.h_v; #endif dof_Measurement = measurement_runtime::DOF; dof_Measurement_noise = dyn_share.R.rows(); vectorized_state dx, dx_new; x_.boxminus(dx, x_propagated); dx_new = dx; if(! (dyn_share.valid)) { continue; } P_ = P_propagated; Matrix res_temp_SO3; MTK::vect<3, scalar_type> seg_SO3; for (std::vector >::iterator it = x_.SO3_state.begin(); it != x_.SO3_state.end(); it++) { int idx = (*it).first; int dim = (*it).second; for(int i = 0; i < 3; i++){ seg_SO3(i) = dx(idx+i); } res_temp_SO3 = MTK::A_matrix(seg_SO3).transpose(); dx_new.template block<3, 1>(idx, 0) = res_temp_SO3 * dx_new.template block<3, 1>(idx, 0); for(int i = 0; i < n; i++){ P_. template block<3, 1>(idx, i) = res_temp_SO3 * (P_. template block<3, 1>(idx, i)); } for(int i = 0; i < n; i++){ P_. template block<1, 3>(i, idx) =(P_. template block<1, 3>(i, idx)) * res_temp_SO3.transpose(); } } Matrix res_temp_S2; MTK::vect<2, scalar_type> seg_S2; for (std::vector >::iterator it = x_.S2_state.begin(); it != x_.S2_state.end(); it++) { int idx = (*it).first; int dim = (*it).second; for(int i = 0; i < 2; i++){ seg_S2(i) = dx(idx + i); } Eigen::Matrix Nx; Eigen::Matrix Mx; x_.S2_Nx_yy(Nx, idx); x_propagated.S2_Mx(Mx, seg_S2, idx); res_temp_S2 = Nx * Mx; dx_new.template block<2, 1>(idx, 0) = res_temp_S2 * dx_new.template block<2, 1>(idx, 0); for(int i = 0; i < n; i++){ P_. template block<2, 1>(idx, i) = res_temp_S2 * (P_. template block<2, 1>(idx, i)); } for(int i = 0; i < n; i++){ P_. template block<1, 2>(i, idx) = (P_. template block<1, 2>(i, idx)) * res_temp_S2.transpose(); } } Matrix K_; if(n > dof_Measurement) { #ifdef USE_sparse Matrix K_temp = h_x * P_ * h_x.transpose(); spMt R_temp = h_v * R_ * h_v.transpose(); K_temp += R_temp; K_ = P_ * h_x.transpose() * K_temp.inverse(); #else K_= P_ * h_x.transpose() * (h_x * P_ * h_x.transpose() + h_v * R * h_v.transpose()).inverse(); #endif } else { #ifdef USE_sparse Eigen::Matrix b = Eigen::Matrix::Identity(dof_Measurement_noise, dof_Measurement_noise); Eigen::SparseQR, Eigen::COLAMDOrdering> solver; solver.compute(R_); Eigen::Matrix R_in_temp = solver.solve(b); spMt R_in =R_in_temp.sparseView(); spMt K_temp = h_x.transpose() * R_in * h_x; cov P_temp = P_.inverse(); P_temp += K_temp; K_ = P_temp.inverse() * h_x.transpose() * R_in; #else Eigen::Matrix R_in = (h_v*R*h_v.transpose()).inverse(); K_ = (h_x.transpose() * R_in * h_x + P_.inverse()).inverse() * h_x.transpose() * R_in; #endif } cov K_x = K_ * h_x; Eigen::Matrix innovation; z.boxminus(innovation, h_); Matrix dx_ = K_ * innovation + (K_x - Matrix::Identity()) * dx_new; state x_before = x_; x_.boxplus(dx_); dyn_share.converge = true; for(int i = 0; i < n ; i++) { if(std::fabs(dx_[i]) > limit[i]) { dyn_share.converge = false; break; } } if(dyn_share.converge) t++; if(t > 1 || i == maximum_iter - 1) { L_ = P_; std::cout << "iteration time:" << t << "," << i << std::endl; Matrix res_temp_SO3; MTK::vect<3, scalar_type> seg_SO3; for(typename std::vector >::iterator it = x_.SO3_state.begin(); it != x_.SO3_state.end(); it++) { int idx = (*it).first; for(int i = 0; i < 3; i++){ seg_SO3(i) = dx_(i + idx); } res_temp_SO3 = MTK::A_matrix(seg_SO3).transpose(); for(int i = 0; i < int(n); i++){ L_. template block<3, 1>(idx, i) = res_temp_SO3 * (P_. template block<3, 1>(idx, i)); } if(n > dof_Measurement) { for(int i = 0; i < dof_Measurement; i++){ K_. template block<3, 1>(idx, i) = res_temp_SO3 * (K_. template block<3, 1>(idx, i)); } } else { for(int i = 0; i < n; i++){ K_x. template block<3, 1>(idx, i) = res_temp_SO3 * (K_x. template block<3, 1>(idx, i)); } } for(int i = 0; i < n; i++){ L_. template block<1, 3>(i, idx) = (L_. template block<1, 3>(i, idx)) * res_temp_SO3.transpose(); P_. template block<1, 3>(i, idx) = (P_. template block<1, 3>(i, idx)) * res_temp_SO3.transpose(); } } Matrix res_temp_S2; MTK::vect<2, scalar_type> seg_S2; for(typename std::vector >::iterator it = x_.S2_state.begin(); it != x_.S2_state.end(); it++) { int idx = (*it).first; for(int i = 0; i < 2; i++){ seg_S2(i) = dx_(i + idx); } Eigen::Matrix Nx; Eigen::Matrix Mx; x_.S2_Nx_yy(Nx, idx); x_propagated.S2_Mx(Mx, seg_S2, idx); res_temp_S2 = Nx * Mx; for(int i = 0; i < n; i++){ L_. template block<2, 1>(idx, i) = res_temp_S2 * (P_. template block<2, 1>(idx, i)); } if(n > dof_Measurement) { for(int i = 0; i < dof_Measurement; i++){ K_. template block<2, 1>(idx, i) = res_temp_S2 * (K_. template block<2, 1>(idx, i)); } } else { for(int i = 0; i < n; i++){ K_x. template block<2, 1>(idx, i) = res_temp_S2 * (K_x. template block<2, 1>(idx, i)); } } for(int i = 0; i < n; i++){ L_. template block<1, 2>(i, idx) = (L_. template block<1, 2>(i, idx)) * res_temp_S2.transpose(); P_. template block<1, 2>(i, idx) = (P_. template block<1, 2>(i, idx)) * res_temp_S2.transpose(); } } if(n > dof_Measurement) { P_ = L_ - K_*h_x * P_; } else { P_ = L_ - K_x * P_; } return; } } } //iterated error state EKF update modified for one specific system. void update_iterated_dyn_share_modified(double R, double &solve_time) { dyn_share_datastruct dyn_share; dyn_share.valid = true; dyn_share.converge = true; int t = 0; state x_propagated = x_; //propagated state x cov P_propagated = P_; //propagated P int dof_Measurement; Matrix K_h; // n(state::DOF) * 1 Matrix K_x; // n * n vectorized_state dx_new = vectorized_state::Zero(); //error state for(int i=-1; i h = h_dyn_share(x_, dyn_share); #ifdef USE_sparse spMt h_x_ = dyn_share.h_x.sparseView(); #else Eigen::Matrix h_x_ = dyn_share.h_x; // partial differention matrices, Jacobian #endif double solve_start = omp_get_wtime(); dof_Measurement = h_x_.rows(); vectorized_state dx; // error x_.boxminus(dx, x_propagated); //get error dx dx_new = dx; //record error state P_ = P_propagated;//record propagated P Matrix res_temp_SO3; MTK::vect<3, scalar_type> seg_SO3; for (std::vector >::iterator it = x_.SO3_state.begin(); it != x_.SO3_state.end(); it++) { int idx = (*it).first; int dim = (*it).second; for(int i = 0; i < 3; i++){ seg_SO3(i) = dx(idx+i); } res_temp_SO3 = MTK::A_matrix(seg_SO3).transpose(); // A, equation (6) in FASTLIO dx_new.template block<3, 1>(idx, 0) = res_temp_SO3 * dx_new.template block<3, 1>(idx, 0); //update SO3 error state //using equation (16) in FASTLIO, update P_ = J * P_ * J^-1 // A * P_(row), update P_ corresponding to the error state for(int i = 0; i < n; i++){ P_. template block<3, 1>(idx, i) = res_temp_SO3 * (P_. template block<3, 1>(idx, i)); } for(int i = 0; i < n; i++){ P_. template block<1, 3>(i, idx) =(P_. template block<1, 3>(i, idx)) * res_temp_SO3.transpose(); // P_(col) * A^T } } // maybe gravity Matrix res_temp_S2; MTK::vect<2, scalar_type> seg_S2; for (std::vector >::iterator it = x_.S2_state.begin(); it != x_.S2_state.end(); it++) { int idx = (*it).first; int dim = (*it).second; for(int i = 0; i < 2; i++){ seg_S2(i) = dx(idx + i); } Eigen::Matrix Nx; Eigen::Matrix Mx; x_.S2_Nx_yy(Nx, idx); x_propagated.S2_Mx(Mx, seg_S2, idx); res_temp_S2 = Nx * Mx; dx_new.template block<2, 1>(idx, 0) = res_temp_S2 * dx_new.template block<2, 1>(idx, 0); for(int i = 0; i < n; i++){ P_. template block<2, 1>(idx, i) = res_temp_S2 * (P_. template block<2, 1>(idx, i)); } for(int i = 0; i < n; i++){ P_. template block<1, 2>(i, idx) = (P_. template block<1, 2>(i, idx)) * res_temp_S2.transpose(); } } //Matrix K_; //Matrix K_h; //Matrix K_x; /* if(n > dof_Measurement) { K_= P_ * h_x_.transpose() * (h_x_ * P_ * h_x_.transpose()/R + Eigen::Matrix::Identity(dof_Measurement, dof_Measurement)).inverse()/R; } else { K_= (h_x_.transpose() * h_x_ + (P_/R).inverse()).inverse()*h_x_.transpose(); } */ if(n > dof_Measurement) { //#ifdef USE_sparse //Matrix K_temp = h_x * P_ * h_x.transpose(); //spMt R_temp = h_v * R_ * h_v.transpose(); //K_temp += R_temp; Eigen::Matrix h_x_cur = Eigen::Matrix::Zero(dof_Measurement, n); h_x_cur.topLeftCorner(dof_Measurement, 12) = h_x_; //Jacobian /* h_x_cur.col(0) = h_x_.col(0); h_x_cur.col(1) = h_x_.col(1); h_x_cur.col(2) = h_x_.col(2); h_x_cur.col(3) = h_x_.col(3); h_x_cur.col(4) = h_x_.col(4); h_x_cur.col(5) = h_x_.col(5); h_x_cur.col(6) = h_x_.col(6); h_x_cur.col(7) = h_x_.col(7); h_x_cur.col(8) = h_x_.col(8); h_x_cur.col(9) = h_x_.col(9); h_x_cur.col(10) = h_x_.col(10); h_x_cur.col(11) = h_x_.col(11); */ Matrix K_ = P_ * h_x_cur.transpose() * (h_x_cur * P_ * h_x_cur.transpose()/R + Eigen::Matrix::Identity(dof_Measurement, dof_Measurement)).inverse()/R; K_h = K_ * dyn_share.h; // K × h(distance) K_x = K_ * h_x_cur; // K * H //#else // K_= P_ * h_x.transpose() * (h_x * P_ * h_x.transpose() + h_v * R * h_v.transpose()).inverse(); //#endif } else { #ifdef USE_sparse //Eigen::Matrix b = Eigen::Matrix::Identity(); //Eigen::SparseQR, Eigen::COLAMDOrdering> solver; spMt A = h_x_.transpose() * h_x_; cov P_temp = (P_/R).inverse(); P_temp. template block<12, 12>(0, 0) += A; P_temp = P_temp.inverse(); /* Eigen::Matrix h_x_cur = Eigen::Matrix::Zero(dof_Measurement, n); h_x_cur.col(0) = h_x_.col(0); h_x_cur.col(1) = h_x_.col(1); h_x_cur.col(2) = h_x_.col(2); h_x_cur.col(3) = h_x_.col(3); h_x_cur.col(4) = h_x_.col(4); h_x_cur.col(5) = h_x_.col(5); h_x_cur.col(6) = h_x_.col(6); h_x_cur.col(7) = h_x_.col(7); h_x_cur.col(8) = h_x_.col(8); h_x_cur.col(9) = h_x_.col(9); h_x_cur.col(10) = h_x_.col(10); h_x_cur.col(11) = h_x_.col(11); */ K_ = P_temp. template block(0, 0) * h_x_.transpose(); K_x = cov::Zero(); K_x. template block(0, 0) = P_inv. template block(0, 0) * HTH; /* solver.compute(R_); Eigen::Matrix R_in_temp = solver.solve(b); spMt R_in =R_in_temp.sparseView(); spMt K_temp = h_x.transpose() * R_in * h_x; cov P_temp = P_.inverse(); P_temp += K_temp; K_ = P_temp.inverse() * h_x.transpose() * R_in; */ #else cov P_temp = (P_/R).inverse(); //(P / R)^-1 = R * P^-1, equation (20) //Eigen::Matrix h_T = h_x_.transpose(); Eigen::Matrix HTH = h_x_.transpose() * h_x_; P_temp. template block<12, 12>(0, 0) += HTH; // (H^T * H + R * P^-1) /* Eigen::Matrix h_x_cur = Eigen::Matrix::Zero(dof_Measurement, n); //std::cout << "line 1767" << std::endl; h_x_cur.col(0) = h_x_.col(0); h_x_cur.col(1) = h_x_.col(1); h_x_cur.col(2) = h_x_.col(2); h_x_cur.col(3) = h_x_.col(3); h_x_cur.col(4) = h_x_.col(4); h_x_cur.col(5) = h_x_.col(5); h_x_cur.col(6) = h_x_.col(6); h_x_cur.col(7) = h_x_.col(7); h_x_cur.col(8) = h_x_.col(8); h_x_cur.col(9) = h_x_.col(9); h_x_cur.col(10) = h_x_.col(10); h_x_cur.col(11) = h_x_.col(11); */ cov P_inv = P_temp.inverse(); // (H_T_H + R * P^-1)^-1 //std::cout << "line 1781" << std::endl; /// R * (H_T_H + P^-1)^-1 * H^T * R^-1 = (H_T_H + P^-1)^-1 * H^T, since R is a scalar here K_h = P_inv. template block(0, 0) * h_x_.transpose() * dyn_share.h; // (H_T_H + P^-1)^-1 * H^T * h(distance) = K * z //std::cout << "line 1780" << std::endl; //cov HTH_cur = cov::Zero(); //HTH_cur. template block<12, 12>(0, 0) = HTH; K_x.setZero(); // = cov::Zero(); K_x. template block(0, 0) = P_inv. template block(0, 0) * HTH; // (H_T_H + R * P^-1)^-1 * H_T_H = KH //K_= (h_x_.transpose() * h_x_ + (P_/R).inverse()).inverse()*h_x_.transpose(); #endif } //K_x = K_ * h_x_; Matrix dx_ = K_h + (K_x - Matrix::Identity()) * dx_new; state x_before = x_; x_.boxplus(dx_); dyn_share.converge = true; for(int i = 0; i < n ; i++) { if(std::fabs(dx_[i]) > limit[i]) { dyn_share.converge = false; break; } } if(dyn_share.converge) t++; if(!t && i == maximum_iter - 2) { dyn_share.converge = true; } if(t > 1 || i == maximum_iter - 1) { L_ = P_; //std::cout << "iteration time" << t << "," << i << std::endl; Matrix res_temp_SO3; MTK::vect<3, scalar_type> seg_SO3; for(typename std::vector >::iterator it = x_.SO3_state.begin(); it != x_.SO3_state.end(); it++) { int idx = (*it).first; for(int i = 0; i < 3; i++){ seg_SO3(i) = dx_(i + idx); } res_temp_SO3 = MTK::A_matrix(seg_SO3).transpose(); for(int i = 0; i < n; i++){ L_. template block<3, 1>(idx, i) = res_temp_SO3 * (P_. template block<3, 1>(idx, i)); } // if(n > dof_Measurement) // { // for(int i = 0; i < dof_Measurement; i++){ // K_.template block<3, 1>(idx, i) = res_temp_SO3 * (K_. template block<3, 1>(idx, i)); // } // } // else // { for(int i = 0; i < 12; i++){ K_x. template block<3, 1>(idx, i) = res_temp_SO3 * (K_x. template block<3, 1>(idx, i)); } //} for(int i = 0; i < n; i++){ L_. template block<1, 3>(i, idx) = (L_. template block<1, 3>(i, idx)) * res_temp_SO3.transpose(); P_. template block<1, 3>(i, idx) = (P_. template block<1, 3>(i, idx)) * res_temp_SO3.transpose(); } } Matrix res_temp_S2; MTK::vect<2, scalar_type> seg_S2; for(typename std::vector >::iterator it = x_.S2_state.begin(); it != x_.S2_state.end(); it++) { int idx = (*it).first; for(int i = 0; i < 2; i++){ seg_S2(i) = dx_(i + idx); } Eigen::Matrix Nx; Eigen::Matrix Mx; x_.S2_Nx_yy(Nx, idx); x_propagated.S2_Mx(Mx, seg_S2, idx); res_temp_S2 = Nx * Mx; for(int i = 0; i < n; i++){ L_. template block<2, 1>(idx, i) = res_temp_S2 * (P_. template block<2, 1>(idx, i)); } // if(n > dof_Measurement) // { // for(int i = 0; i < dof_Measurement; i++){ // K_. template block<2, 1>(idx, i) = res_temp_S2 * (K_. template block<2, 1>(idx, i)); // } // } // else // { for(int i = 0; i < 12; i++){ K_x. template block<2, 1>(idx, i) = res_temp_S2 * (K_x. template block<2, 1>(idx, i)); } //} for(int i = 0; i < n; i++){ L_. template block<1, 2>(i, idx) = (L_. template block<1, 2>(i, idx)) * res_temp_S2.transpose(); P_. template block<1, 2>(i, idx) = (P_. template block<1, 2>(i, idx)) * res_temp_S2.transpose(); } } // if(n > dof_Measurement) // { // Eigen::Matrix h_x_cur = Eigen::Matrix::Zero(dof_Measurement, n); // h_x_cur.topLeftCorner(dof_Measurement, 12) = h_x_; // /* // h_x_cur.col(0) = h_x_.col(0); // h_x_cur.col(1) = h_x_.col(1); // h_x_cur.col(2) = h_x_.col(2); // h_x_cur.col(3) = h_x_.col(3); // h_x_cur.col(4) = h_x_.col(4); // h_x_cur.col(5) = h_x_.col(5); // h_x_cur.col(6) = h_x_.col(6); // h_x_cur.col(7) = h_x_.col(7); // h_x_cur.col(8) = h_x_.col(8); // h_x_cur.col(9) = h_x_.col(9); // h_x_cur.col(10) = h_x_.col(10); // h_x_cur.col(11) = h_x_.col(11); // */ // P_ = L_ - K_*h_x_cur * P_; // } // else //{ P_ = L_ - K_x.template block(0, 0) * P_.template block<12, n>(0, 0); //} solve_time += omp_get_wtime() - solve_start; return; } solve_time += omp_get_wtime() - solve_start; } } void change_x(state &input_state) { x_ = input_state; if((!x_.vect_state.size())&&(!x_.SO3_state.size())&&(!x_.S2_state.size())) { x_.build_S2_state(); x_.build_SO3_state(); x_.build_vect_state(); } } void change_P(cov &input_cov) { P_ = input_cov; } const state& get_x() const { return x_; } const cov& get_P() const { return P_; } private: state x_; measurement m_; cov P_; spMt l_; spMt f_x_1; spMt f_x_2; cov F_x1 = cov::Identity(); cov F_x2 = cov::Identity(); cov L_ = cov::Identity(); processModel *f; processMatrix1 *f_x; processMatrix2 *f_w; measurementModel *h; measurementMatrix1 *h_x; measurementMatrix2 *h_v; measurementModel_dyn *h_dyn; measurementMatrix1_dyn *h_x_dyn; measurementMatrix2_dyn *h_v_dyn; measurementModel_share *h_share; measurementModel_dyn_share *h_dyn_share; int maximum_iter = 0; scalar_type limit[n]; template T check_safe_update( T _temp_vec ) { T temp_vec = _temp_vec; if ( std::isnan( temp_vec(0, 0) ) ) { temp_vec.setZero(); return temp_vec; } double angular_dis = temp_vec.block( 0, 0, 3, 1 ).norm() * 57.3; double pos_dis = temp_vec.block( 3, 0, 3, 1 ).norm(); if ( angular_dis >= 20 || pos_dis > 1 ) { printf( "Angular dis = %.2f, pos dis = %.2f\r\n", angular_dis, pos_dis ); temp_vec.setZero(); } return temp_vec; } public: EIGEN_MAKE_ALIGNED_OPERATOR_NEW }; } // namespace esekfom #endif // ESEKFOM_EKF_HPP ================================================ FILE: include/IKFoM_toolkit/esekfom/util.hpp ================================================ /* * Copyright (c) 2019--2023, The University of Hong Kong * All rights reserved. * * Author: Dongjiao HE * * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions * are met: * * * Redistributions of source code must retain the above copyright * notice, this list of conditions and the following disclaimer. * * Redistributions in binary form must reproduce the above * copyright notice, this list of conditions and the following * disclaimer in the documentation and/or other materials provided * with the distribution. * * Neither the name of the Universitaet Bremen nor the names of its * contributors may be used to endorse or promote products derived * from this software without specific prior written permission. * * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE * POSSIBILITY OF SUCH DAMAGE. */ #ifndef __MEKFOM_UTIL_HPP__ #define __MEKFOM_UTIL_HPP__ #include #include "../mtk/src/mtkmath.hpp" namespace esekfom { template class is_same { public: operator bool() { return false; } }; template class is_same { public: operator bool() { return true; } }; template class is_double { public: operator bool() { return false; } }; template<> class is_double { public: operator bool() { return true; } }; template static T id(const T &x) { return x; } } // namespace esekfom #endif // __MEKFOM_UTIL_HPP__ ================================================ FILE: include/IKFoM_toolkit/mtk/build_manifold.hpp ================================================ // This is an advanced implementation of the algorithm described in the // following paper: // C. Hertzberg, R. Wagner, U. Frese, and L. Schroder. Integratinggeneric sensor fusion algorithms with sound state representationsthrough encapsulation of manifolds. // CoRR, vol. abs/1107.1119, 2011.[Online]. Available: http://arxiv.org/abs/1107.1119 /* * Copyright (c) 2019--2023, The University of Hong Kong * All rights reserved. * * Modifier: Dongjiao HE * * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions * are met: * * * Redistributions of source code must retain the above copyright * notice, this list of conditions and the following disclaimer. * * Redistributions in binary form must reproduce the above * copyright notice, this list of conditions and the following * disclaimer in the documentation and/or other materials provided * with the distribution. * * Neither the name of the Universitaet Bremen nor the names of its * contributors may be used to endorse or promote products derived * from this software without specific prior written permission. * * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE * POSSIBILITY OF SUCH DAMAGE. */ /* * Copyright (c) 2008--2011, Universitaet Bremen * All rights reserved. * * Author: Christoph Hertzberg * * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions * are met: * * * Redistributions of source code must retain the above copyright * notice, this list of conditions and the following disclaimer. * * Redistributions in binary form must reproduce the above * copyright notice, this list of conditions and the following * disclaimer in the documentation and/or other materials provided * with the distribution. * * Neither the name of the Universitaet Bremen nor the names of its * contributors may be used to endorse or promote products derived * from this software without specific prior written permission. * * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE * POSSIBILITY OF SUCH DAMAGE. */ /** * @file mtk/build_manifold.hpp * @brief Macro to automatically construct compound manifolds. * */ #ifndef MTK_AUTOCONSTRUCT_HPP_ #define MTK_AUTOCONSTRUCT_HPP_ #include #include #include #include #include "src/SubManifold.hpp" #include "startIdx.hpp" #ifndef PARSED_BY_DOXYGEN //////// internals ////// #define MTK_APPLY_MACRO_ON_TUPLE(r, macro, tuple) macro tuple #define MTK_TRANSFORM_COMMA(macro, entries) BOOST_PP_SEQ_ENUM(BOOST_PP_SEQ_TRANSFORM_S(1, MTK_APPLY_MACRO_ON_TUPLE, macro, entries)) #define MTK_TRANSFORM(macro, entries) BOOST_PP_SEQ_FOR_EACH_R(1, MTK_APPLY_MACRO_ON_TUPLE, macro, entries) #define MTK_CONSTRUCTOR_ARG( type, id) const type& id = type() #define MTK_CONSTRUCTOR_COPY( type, id) id(id) #define MTK_BOXPLUS( type, id) id.boxplus(MTK::subvector(__vec, &self::id), __scale); #define MTK_OPLUS( type, id) id.oplus(MTK::subvector_(__vec, &self::id), __scale); #define MTK_BOXMINUS( type, id) id.boxminus(MTK::subvector(__res, &self::id), __oth.id); #define MTK_S2_hat( type, id) if(id.IDX == idx){id.S2_hat(res);} #define MTK_S2_Nx_yy( type, id) if(id.IDX == idx){id.S2_Nx_yy(res);} #define MTK_S2_Mx( type, id) if(id.IDX == idx){id.S2_Mx(res, dx);} #define MTK_OSTREAM( type, id) << __var.id << " " #define MTK_ISTREAM( type, id) >> __var.id #define MTK_S2_state( type, id) if(id.TYP == 1){S2_state.push_back(std::make_pair(id.IDX, id.DIM));} #define MTK_SO3_state( type, id) if(id.TYP == 2){(SO3_state).push_back(std::make_pair(id.IDX, id.DIM));} #define MTK_vect_state( type, id) if(id.TYP == 0){(vect_state).push_back(std::make_pair(std::make_pair(id.IDX, id.DIM), type::DOF));} #define MTK_SUBVARLIST(seq, S2state, SO3state) \ BOOST_PP_FOR_1( \ ( \ BOOST_PP_SEQ_SIZE(seq), \ BOOST_PP_SEQ_HEAD(seq), \ BOOST_PP_SEQ_TAIL(seq) (~), \ 0,\ 0,\ S2state,\ SO3state ),\ MTK_ENTRIES_TEST, MTK_ENTRIES_NEXT, MTK_ENTRIES_OUTPUT) #define MTK_PUT_TYPE(type, id, dof, dim, S2state, SO3state) \ MTK::SubManifold id; #define MTK_PUT_TYPE_AND_ENUM(type, id, dof, dim, S2state, SO3state) \ MTK_PUT_TYPE(type, id, dof, dim, S2state, SO3state) \ enum {DOF = type::DOF + dof}; \ enum {DIM = type::DIM+dim}; \ typedef type::scalar scalar; #define MTK_ENTRIES_OUTPUT(r, state) MTK_ENTRIES_OUTPUT_I state #define MTK_ENTRIES_OUTPUT_I(s, head, seq, dof, dim, S2state, SO3state) \ MTK_APPLY_MACRO_ON_TUPLE(~, \ BOOST_PP_IF(BOOST_PP_DEC(s), MTK_PUT_TYPE, MTK_PUT_TYPE_AND_ENUM), \ ( BOOST_PP_TUPLE_REM_2 head, dof, dim, S2state, SO3state)) #define MTK_ENTRIES_TEST(r, state) MTK_TUPLE_ELEM_4_0 state //! this used to be BOOST_PP_TUPLE_ELEM_4_0: #define MTK_TUPLE_ELEM_4_0(a,b,c,d,e,f, g) a #define MTK_ENTRIES_NEXT(r, state) MTK_ENTRIES_NEXT_I state #define MTK_ENTRIES_NEXT_I(len, head, seq, dof, dim, S2state, SO3state) ( \ BOOST_PP_DEC(len), \ BOOST_PP_SEQ_HEAD(seq), \ BOOST_PP_SEQ_TAIL(seq), \ dof + BOOST_PP_TUPLE_ELEM_2_0 head::DOF,\ dim + BOOST_PP_TUPLE_ELEM_2_0 head::DIM,\ S2state,\ SO3state) #endif /* not PARSED_BY_DOXYGEN */ /** * Construct a manifold. * @param name is the class-name of the manifold, * @param entries is the list of sub manifolds * * Entries must be given in a list like this: * @code * typedef MTK::trafo > Pose; * typedef MTK::vect Vec3; * MTK_BUILD_MANIFOLD(imu_state, * ((Pose, pose)) * ((Vec3, vel)) * ((Vec3, acc_bias)) * ) * @endcode * Whitespace is optional, but the double parentheses are necessary. * Construction is done entirely in preprocessor. * After construction @a name is also a manifold. Its members can be * accessed by names given in @a entries. * * @note Variable types are not allowed to have commas, thus types like * @c vect need to be typedef'ed ahead. */ #define MTK_BUILD_MANIFOLD(name, entries) \ struct name { \ typedef name self; \ std::vector > S2_state;\ std::vector > SO3_state;\ std::vector, int> > vect_state;\ MTK_SUBVARLIST(entries, S2_state, SO3_state) \ name ( \ MTK_TRANSFORM_COMMA(MTK_CONSTRUCTOR_ARG, entries) \ ) : \ MTK_TRANSFORM_COMMA(MTK_CONSTRUCTOR_COPY, entries) {}\ int getDOF() const { return DOF; } \ void boxplus(const MTK::vectview & __vec, scalar __scale = 1 ) { \ MTK_TRANSFORM(MTK_BOXPLUS, entries)\ } \ void oplus(const MTK::vectview & __vec, scalar __scale = 1 ) { \ MTK_TRANSFORM(MTK_OPLUS, entries)\ } \ void boxminus(MTK::vectview __res, const name& __oth) const { \ MTK_TRANSFORM(MTK_BOXMINUS, entries)\ } \ friend std::ostream& operator<<(std::ostream& __os, const name& __var){ \ return __os MTK_TRANSFORM(MTK_OSTREAM, entries); \ } \ void build_S2_state(){\ MTK_TRANSFORM(MTK_S2_state, entries)\ }\ void build_vect_state(){\ MTK_TRANSFORM(MTK_vect_state, entries)\ }\ void build_SO3_state(){\ MTK_TRANSFORM(MTK_SO3_state, entries)\ }\ void S2_hat(Eigen::Matrix &res, int idx) {\ MTK_TRANSFORM(MTK_S2_hat, entries)\ }\ void S2_Nx_yy(Eigen::Matrix &res, int idx) {\ MTK_TRANSFORM(MTK_S2_Nx_yy, entries)\ }\ void S2_Mx(Eigen::Matrix &res, Eigen::Matrix dx, int idx) {\ MTK_TRANSFORM(MTK_S2_Mx, entries)\ }\ friend std::istream& operator>>(std::istream& __is, name& __var){ \ return __is MTK_TRANSFORM(MTK_ISTREAM, entries); \ } \ }; #endif /*MTK_AUTOCONSTRUCT_HPP_*/ ================================================ FILE: include/IKFoM_toolkit/mtk/src/SubManifold.hpp ================================================ // This is an advanced implementation of the algorithm described in the // following paper: // C. Hertzberg, R. Wagner, U. Frese, and L. Schroder. Integratinggeneric sensor fusion algorithms with sound state representationsthrough encapsulation of manifolds. // CoRR, vol. abs/1107.1119, 2011.[Online]. Available: http://arxiv.org/abs/1107.1119 /* * Copyright (c) 2019--2023, The University of Hong Kong * All rights reserved. * * Modifier: Dongjiao HE * * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions * are met: * * * Redistributions of source code must retain the above copyright * notice, this list of conditions and the following disclaimer. * * Redistributions in binary form must reproduce the above * copyright notice, this list of conditions and the following * disclaimer in the documentation and/or other materials provided * with the distribution. * * Neither the name of the Universitaet Bremen nor the names of its * contributors may be used to endorse or promote products derived * from this software without specific prior written permission. * * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE * POSSIBILITY OF SUCH DAMAGE. */ /* * Copyright (c) 2008--2011, Universitaet Bremen * All rights reserved. * * Author: Christoph Hertzberg * * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions * are met: * * * Redistributions of source code must retain the above copyright * notice, this list of conditions and the following disclaimer. * * Redistributions in binary form must reproduce the above * copyright notice, this list of conditions and the following * disclaimer in the documentation and/or other materials provided * with the distribution. * * Neither the name of the Universitaet Bremen nor the names of its * contributors may be used to endorse or promote products derived * from this software without specific prior written permission. * * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE * POSSIBILITY OF SUCH DAMAGE. */ /** * @file mtk/src/SubManifold.hpp * @brief Defines the SubManifold class */ #ifndef SUBMANIFOLD_HPP_ #define SUBMANIFOLD_HPP_ #include "vectview.hpp" namespace MTK { /** * @ingroup SubManifolds * Helper class for compound manifolds. * This class wraps a manifold T and provides an enum IDX refering to the * index of the SubManifold within the compound manifold. * * Memberpointers to a submanifold can be used for @ref SubManifolds "functions accessing submanifolds". * * @tparam T The manifold type of the sub-type * @tparam idx The index of the sub-type within the compound manifold */ template struct SubManifold : public T { enum {IDX = idx, DIM = dim /*!< index of the sub-type within the compound manifold */ }; //! manifold type typedef T type; //! Construct from derived type template explicit SubManifold(const X& t) : T(t) {}; //! Construct from internal type //explicit SubManifold(const T& t) : T(t) {}; //! inherit assignment operator using T::operator=; }; } // namespace MTK #endif /* SUBMANIFOLD_HPP_ */ ================================================ FILE: include/IKFoM_toolkit/mtk/src/mtkmath.hpp ================================================ // This is an advanced implementation of the algorithm described in the // following paper: // C. Hertzberg, R. Wagner, U. Frese, and L. Schroder. Integratinggeneric sensor fusion algorithms with sound state representationsthrough encapsulation of manifolds. // CoRR, vol. abs/1107.1119, 2011.[Online]. Available: http://arxiv.org/abs/1107.1119 /* * Copyright (c) 2019--2023, The University of Hong Kong * All rights reserved. * * Modifier: Dongjiao HE * * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions * are met: * * * Redistributions of source code must retain the above copyright * notice, this list of conditions and the following disclaimer. * * Redistributions in binary form must reproduce the above * copyright notice, this list of conditions and the following * disclaimer in the documentation and/or other materials provided * with the distribution. * * Neither the name of the Universitaet Bremen nor the names of its * contributors may be used to endorse or promote products derived * from this software without specific prior written permission. * * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE * POSSIBILITY OF SUCH DAMAGE. */ /* * Copyright (c) 2008--2011, Universitaet Bremen * All rights reserved. * * Author: Christoph Hertzberg * * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions * are met: * * * Redistributions of source code must retain the above copyright * notice, this list of conditions and the following disclaimer. * * Redistributions in binary form must reproduce the above * copyright notice, this list of conditions and the following * disclaimer in the documentation and/or other materials provided * with the distribution. * * Neither the name of the Universitaet Bremen nor the names of its * contributors may be used to endorse or promote products derived * from this software without specific prior written permission. * * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE * POSSIBILITY OF SUCH DAMAGE. */ /** * @file mtk/src/mtkmath.hpp * @brief several math utility functions. */ #ifndef MTKMATH_H_ #define MTKMATH_H_ #include #include #include "../types/vect.hpp" #ifndef M_PI #define M_PI 3.1415926535897932384626433832795 #endif namespace MTK { namespace internal { template struct traits { typedef typename Manifold::scalar scalar; enum {DOF = Manifold::DOF}; typedef vect vectorized_type; typedef Eigen::Matrix matrix_type; }; template<> struct traits : traits > {}; template<> struct traits : traits > {}; } // namespace internal /** * \defgroup MTKMath Mathematical helper functions */ //@{ //! constant @f$ \pi @f$ const double pi = M_PI; template inline scalar tolerance(); template<> inline float tolerance() { return 1e-5f; } template<> inline double tolerance() { return 1e-11; } /** * normalize @a x to @f$[-bound, bound] @f$. * * result for @f$ x = bound + 2\cdot n\cdot bound @f$ is arbitrary @f$\pm bound @f$. */ template inline scalar normalize(scalar x, scalar bound){ //not used if(std::fabs(x) <= bound) return x; int r = (int)(x *(scalar(1.0)/ bound)); return x - ((r + (r>>31) + 1) & ~1)*bound; } /** * Calculate cosine and sinc of sqrt(x2). * @param x2 the squared angle must be non-negative * @return a pair containing cos and sinc of sqrt(x2) */ template std::pair cos_sinc_sqrt(const scalar &x2){ using std::sqrt; using std::cos; using std::sin; static scalar const taylor_0_bound = boost::math::tools::epsilon(); static scalar const taylor_2_bound = sqrt(taylor_0_bound); static scalar const taylor_n_bound = sqrt(taylor_2_bound); assert(x2>=0 && "argument must be non-negative"); // FIXME check if bigger bounds are possible if(x2>=taylor_n_bound) { // slow fall-back solution scalar x = sqrt(x2); return std::make_pair(cos(x), sin(x)/x); // x is greater than 0. } // FIXME Replace by Horner-Scheme (4 instead of 5 FLOP/term, numerically more stable, theoretically cos and sinc can be calculated in parallel using SSE2 mulpd/addpd) // TODO Find optimal coefficients using Remez algorithm static scalar const inv[] = {1/3., 1/4., 1/5., 1/6., 1/7., 1/8., 1/9.}; scalar cosi = 1., sinc=1; scalar term = -1/2. * x2; for(int i=0; i<3; ++i) { cosi += term; term *= inv[2*i]; sinc += term; term *= -inv[2*i+1] * x2; } return std::make_pair(cosi, sinc); } template Eigen::Matrix hat(const Base& v) { Eigen::Matrix res; res << 0, -v[2], v[1], v[2], 0, -v[0], -v[1], v[0], 0; return res; } template Eigen::Matrix A_inv_trans(const Base& v){ Eigen::Matrix res; if(v.norm() > MTK::tolerance()) { res = Eigen::Matrix::Identity() + 0.5 * hat(v) + (1 - v.norm() * std::cos(v.norm() / 2) / 2 / std::sin(v.norm() / 2)) * hat(v) * hat(v) / v.squaredNorm(); } else { res = Eigen::Matrix::Identity(); } return res; } template Eigen::Matrix A_inv(const Base& v){ Eigen::Matrix res; if(v.norm() > MTK::tolerance()) { res = Eigen::Matrix::Identity() - 0.5 * hat(v) + (1 - v.norm() * std::cos(v.norm() / 2) / 2 / std::sin(v.norm() / 2)) * hat(v) * hat(v) / v.squaredNorm(); } else { res = Eigen::Matrix::Identity(); } return res; } template Eigen::Matrix S2_w_expw_( Eigen::Matrix v, scalar length) { Eigen::Matrix res; scalar norm = std::sqrt(v[0]*v[0] + v[1]*v[1]); if(norm < MTK::tolerance()){ res = Eigen::Matrix::Zero(); res(0, 1) = 1; res(1, 2) = 1; res /= length; } else{ res << -v[0]*(1/norm-1/std::tan(norm))/std::sin(norm), norm/std::sin(norm), 0, -v[1]*(1/norm-1/std::tan(norm))/std::sin(norm), 0, norm/std::sin(norm); res /= length; } } template Eigen::Matrix A_matrix(const Base & v){ Eigen::Matrix res; double squaredNorm = v[0] * v[0] + v[1] * v[1] + v[2] * v[2]; double norm = std::sqrt(squaredNorm); if(norm < MTK::tolerance()){ res = Eigen::Matrix::Identity(); } else{ // I + (1 - cos(norm)) / norm^2 * v^ + (1 - sin(norm) / norm) / norm^2 * v^ *v^ res = Eigen::Matrix::Identity() + (1 - std::cos(norm)) / squaredNorm * hat(v) + (1 - std::sin(norm) / norm) / squaredNorm * hat(v) * hat(v); } return res; } template scalar exp(vectview result, vectview vec, const scalar& scale = 1) { scalar norm2 = vec.squaredNorm(); std::pair cos_sinc = cos_sinc_sqrt(scale*scale * norm2); scalar mult = cos_sinc.second * scale; result = mult * vec; return cos_sinc.first; } /** * Inverse function to @c exp. * * @param result @c vectview to the result * @param w scalar part of input * @param vec vector part of input * @param scale scale result by this value * @param plus_minus_periodicity if true values @f$[w, vec]@f$ and @f$[-w, -vec]@f$ give the same result */ template void log(vectview result, const scalar &w, const vectview vec, const scalar &scale, bool plus_minus_periodicity) { // FIXME implement optimized case for vec.squaredNorm() <= tolerance() * (w*w) via Rational Remez approximation ~> only one division scalar nv = vec.norm(); if(nv < tolerance()) { if(!plus_minus_periodicity && w < 0) { // find the maximal entry: int i; nv = vec.cwiseAbs().maxCoeff(&i); result = scale * std::atan2(nv, w) * vect::Unit(i); return; } nv = tolerance(); } scalar s = scale / nv * (plus_minus_periodicity ? std::atan(nv / w) : std::atan2(nv, w) ); result = s * vec; } } // namespace MTK #endif /* MTKMATH_H_ */ ================================================ FILE: include/IKFoM_toolkit/mtk/src/vectview.hpp ================================================ /* * Copyright (c) 2008--2011, Universitaet Bremen * All rights reserved. * * Author: Christoph Hertzberg * * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions * are met: * * * Redistributions of source code must retain the above copyright * notice, this list of conditions and the following disclaimer. * * Redistributions in binary form must reproduce the above * copyright notice, this list of conditions and the following * disclaimer in the documentation and/or other materials provided * with the distribution. * * Neither the name of the Universitaet Bremen nor the names of its * contributors may be used to endorse or promote products derived * from this software without specific prior written permission. * * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE * POSSIBILITY OF SUCH DAMAGE. */ /** * @file mtk/src/vectview.hpp * @brief Wrapper class around a pointer used as interface for plain vectors. */ #ifndef VECTVIEW_HPP_ #define VECTVIEW_HPP_ #include namespace MTK { /** * A view to a vector. * Essentially, @c vectview is only a pointer to @c scalar but can be used directly in @c Eigen expressions. * The dimension of the vector is given as template parameter and type-checked when used in expressions. * Data has to be modifiable. * * @tparam scalar Scalar type of the vector. * @tparam dim Dimension of the vector. * * @todo @c vectview can be replaced by simple inheritance of @c Eigen::Map, as soon as they get const-correct */ namespace internal { template struct CovBlock { typedef typename Eigen::Block, T1::DOF, T2::DOF> Type; typedef typename Eigen::Block, T1::DOF, T2::DOF> ConstType; }; template struct CovBlock_ { typedef typename Eigen::Block, T1::DIM, T2::DIM> Type; typedef typename Eigen::Block, T1::DIM, T2::DIM> ConstType; }; template struct CrossCovBlock { typedef typename Eigen::Block, T1::DOF, T2::DOF> Type; typedef typename Eigen::Block, T1::DOF, T2::DOF> ConstType; }; template struct CrossCovBlock_ { typedef typename Eigen::Block, T1::DIM, T2::DIM> Type; typedef typename Eigen::Block, T1::DIM, T2::DIM> ConstType; }; template struct VectviewBase { typedef Eigen::Matrix matrix_type; typedef typename matrix_type::MapType Type; typedef typename matrix_type::ConstMapType ConstType; }; template struct UnalignedType { typedef T type; }; } template class vectview : public internal::VectviewBase::Type { typedef internal::VectviewBase VectviewBase; public: //! plain matrix type typedef typename VectviewBase::matrix_type matrix_type; //! base type typedef typename VectviewBase::Type base; //! construct from pointer explicit vectview(scalar* data, int dim_=dim) : base(data, dim_) {} //! construct from plain matrix vectview(matrix_type& m) : base(m.data(), m.size()) {} //! construct from another @c vectview vectview(const vectview &v) : base(v) {} //! construct from Eigen::Block: template vectview(Eigen::VectorBlock block) : base(&block.coeffRef(0), block.size()) {} template vectview(Eigen::Block block) : base(&block.coeffRef(0), block.size()) {} //! inherit assignment operator using base::operator=; //! data pointer scalar* data() {return const_cast(base::data());} }; /** * @c const version of @c vectview. * Compared to @c Eigen::Map this implementation is const correct, i.e., * data will not be modifiable using this view. * * @tparam scalar Scalar type of the vector. * @tparam dim Dimension of the vector. * * @sa vectview */ template class vectview : public internal::VectviewBase::ConstType { typedef internal::VectviewBase VectviewBase; public: //! plain matrix type typedef typename VectviewBase::matrix_type matrix_type; //! base type typedef typename VectviewBase::ConstType base; //! construct from const pointer explicit vectview(const scalar* data, int dim_ = dim) : base(data, dim_) {} //! construct from column vector template vectview(const Eigen::Matrix& m) : base(m.data()) {} //! construct from row vector template vectview(const Eigen::Matrix& m) : base(m.data()) {} //! construct from another @c vectview vectview(vectview x) : base(x.data()) {} //! construct from base vectview(const base &x) : base(x) {} /** * Construct from Block * @todo adapt this, when Block gets const-correct */ template vectview(Eigen::VectorBlock block) : base(&block.coeffRef(0)) {} template vectview(Eigen::Block block) : base(&block.coeffRef(0)) {} }; } // namespace MTK #endif /* VECTVIEW_HPP_ */ ================================================ FILE: include/IKFoM_toolkit/mtk/startIdx.hpp ================================================ // This is an advanced implementation of the algorithm described in the // following paper: // C. Hertzberg, R. Wagner, U. Frese, and L. Schroder. Integratinggeneric sensor fusion algorithms with sound state representationsthrough encapsulation of manifolds. // CoRR, vol. abs/1107.1119, 2011.[Online]. Available: http://arxiv.org/abs/1107.1119 /* * Copyright (c) 2019--2023, The University of Hong Kong * All rights reserved. * * Modifier: Dongjiao HE * * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions * are met: * * * Redistributions of source code must retain the above copyright * notice, this list of conditions and the following disclaimer. * * Redistributions in binary form must reproduce the above * copyright notice, this list of conditions and the following * disclaimer in the documentation and/or other materials provided * with the distribution. * * Neither the name of the Universitaet Bremen nor the names of its * contributors may be used to endorse or promote products derived * from this software without specific prior written permission. * * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE * POSSIBILITY OF SUCH DAMAGE. */ /* * Copyright (c) 2008--2011, Universitaet Bremen * All rights reserved. * * Author: Christoph Hertzberg * * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions * are met: * * * Redistributions of source code must retain the above copyright * notice, this list of conditions and the following disclaimer. * * Redistributions in binary form must reproduce the above * copyright notice, this list of conditions and the following * disclaimer in the documentation and/or other materials provided * with the distribution. * * Neither the name of the Universitaet Bremen nor the names of its * contributors may be used to endorse or promote products derived * from this software without specific prior written permission. * * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE * POSSIBILITY OF SUCH DAMAGE. */ /** * @file mtk/startIdx.hpp * @brief Tools to access sub-elements of compound manifolds. */ #ifndef GET_START_INDEX_H_ #define GET_START_INDEX_H_ #include #include "src/SubManifold.hpp" #include "src/vectview.hpp" namespace MTK { /** * \defgroup SubManifolds Accessing Submanifolds * For compound manifolds constructed using MTK_BUILD_MANIFOLD, member pointers * can be used to get sub-vectors or matrix-blocks of a corresponding big matrix. * E.g. for a type @a pose consisting of @a orient and @a trans the member pointers * @c &pose::orient and @c &pose::trans give all required information and are still * valid if the base type gets extended or the actual types of @a orient and @a trans * change (e.g. from 2D to 3D). * * @todo Maybe require manifolds to typedef MatrixType and VectorType, etc. */ //@{ /** * Determine the index of a sub-variable within a compound variable. */ template int getStartIdx( MTK::SubManifold Base::*) { return idx; } template int getStartIdx_( MTK::SubManifold Base::*) { return dim; } /** * Determine the degrees of freedom of a sub-variable within a compound variable. */ template int getDof( MTK::SubManifold Base::*) { return T::DOF; } template int getDim( MTK::SubManifold Base::*) { return T::DIM; } /** * set the diagonal elements of a covariance matrix corresponding to a sub-variable */ template void setDiagonal(Eigen::Matrix &cov, MTK::SubManifold Base::*, const typename Base::scalar &val) { cov.diagonal().template segment(idx).setConstant(val); } template void setDiagonal_(Eigen::Matrix &cov, MTK::SubManifold Base::*, const typename Base::scalar &val) { cov.diagonal().template segment(dim).setConstant(val); } /** * Get the subblock of corresponding to two members, i.e. * \code * Eigen::Matrix m; * MTK::subblock(m, &Pose::orient, &Pose::trans) = some_expression; * MTK::subblock(m, &Pose::trans, &Pose::orient) = some_expression.trans(); * \endcode * lets you modify mixed covariance entries in a bigger covariance matrix. */ template typename MTK::internal::CovBlock::Type subblock(Eigen::Matrix &cov, MTK::SubManifold Base::*, MTK::SubManifold Base::*) { return cov.template block(idx1, idx2); } template typename MTK::internal::CovBlock_::Type subblock_(Eigen::Matrix &cov, MTK::SubManifold Base::*, MTK::SubManifold Base::*) { return cov.template block(dim1, dim2); } template typename MTK::internal::CrossCovBlock::Type subblock(Eigen::Matrix &cov, MTK::SubManifold Base1::*, MTK::SubManifold Base2::*) { return cov.template block(idx1, idx2); } template typename MTK::internal::CrossCovBlock_::Type subblock_(Eigen::Matrix &cov, MTK::SubManifold Base1::*, MTK::SubManifold Base2::*) { return cov.template block(dim1, dim2); } /** * Get the subblock of corresponding to a member, i.e. * \code * Eigen::Matrix m; * MTK::subblock(m, &Pose::orient) = some_expression; * \endcode * lets you modify covariance entries in a bigger covariance matrix. */ template typename MTK::internal::CovBlock_::Type subblock_(Eigen::Matrix &cov, MTK::SubManifold Base::*) { return cov.template block(dim, dim); } template typename MTK::internal::CovBlock::Type subblock(Eigen::Matrix &cov, MTK::SubManifold Base::*) { return cov.template block(idx, idx); } template class get_cov { public: typedef Eigen::Matrix type; typedef const Eigen::Matrix const_type; }; template class get_cov_ { public: typedef Eigen::Matrix type; typedef const Eigen::Matrix const_type; }; template class get_cross_cov { public: typedef Eigen::Matrix type; typedef const type const_type; }; template class get_cross_cov_ { public: typedef Eigen::Matrix type; typedef const type const_type; }; template vectview subvector_impl_(vectview vec, SubManifold Base::*) { return vec.template segment(dim); } template vectview subvector_impl(vectview vec, SubManifold Base::*) { return vec.template segment(idx); } /** * Get the subvector corresponding to a sub-manifold from a bigger vector. */ template vectview subvector_(vectview vec, SubManifold Base::* ptr) { return subvector_impl_(vec, ptr); } template vectview subvector(vectview vec, SubManifold Base::* ptr) { return subvector_impl(vec, ptr); } /** * @todo This should be covered already by subvector(vectview vec,SubManifold Base::*) */ template vectview subvector(Eigen::Matrix& vec, SubManifold Base::* ptr) { return subvector_impl(vectview(vec), ptr); } template vectview subvector_(Eigen::Matrix& vec, SubManifold Base::* ptr) { return subvector_impl_(vectview(vec), ptr); } template vectview subvector_(const Eigen::Matrix& vec, SubManifold Base::* ptr) { return subvector_impl_(vectview(vec), ptr); } template vectview subvector(const Eigen::Matrix& vec, SubManifold Base::* ptr) { return subvector_impl(vectview(vec), ptr); } /** * const version of subvector(vectview vec,SubManifold Base::*) */ template vectview subvector_impl(const vectview cvec, SubManifold Base::*) { return cvec.template segment(idx); } template vectview subvector_impl_(const vectview cvec, SubManifold Base::*) { return cvec.template segment(dim); } template vectview subvector(const vectview cvec, SubManifold Base::* ptr) { return subvector_impl(cvec, ptr); } } // namespace MTK #endif // GET_START_INDEX_H_ ================================================ FILE: include/IKFoM_toolkit/mtk/types/S2.hpp ================================================ // This is a NEW implementation of the algorithm described in the // following paper: // C. Hertzberg, R. Wagner, U. Frese, and L. Schroder. Integratinggeneric sensor fusion algorithms with sound state representationsthrough encapsulation of manifolds. // CoRR, vol. abs/1107.1119, 2011.[Online]. Available: http://arxiv.org/abs/1107.1119 /* * Copyright (c) 2019--2023, The University of Hong Kong * All rights reserved. * * Modifier: Dongjiao HE * * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions * are met: * * * Redistributions of source code must retain the above copyright * notice, this list of conditions and the following disclaimer. * * Redistributions in binary form must reproduce the above * copyright notice, this list of conditions and the following * disclaimer in the documentation and/or other materials provided * with the distribution. * * Neither the name of the Universitaet Bremen nor the names of its * contributors may be used to endorse or promote products derived * from this software without specific prior written permission. * * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE * POSSIBILITY OF SUCH DAMAGE. */ /* * Copyright (c) 2008--2011, Universitaet Bremen * All rights reserved. * * Author: Christoph Hertzberg * * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions * are met: * * * Redistributions of source code must retain the above copyright * notice, this list of conditions and the following disclaimer. * * Redistributions in binary form must reproduce the above * copyright notice, this list of conditions and the following * disclaimer in the documentation and/or other materials provided * with the distribution. * * Neither the name of the Universitaet Bremen nor the names of its * contributors may be used to endorse or promote products derived * from this software without specific prior written permission. * * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE * POSSIBILITY OF SUCH DAMAGE. */ /** * @file mtk/types/S2.hpp * @brief Unit vectors on the sphere, or directions in 3D. */ #ifndef S2_H_ #define S2_H_ #include "vect.hpp" #include "SOn.hpp" #include "../src/mtkmath.hpp" namespace MTK { /** * Manifold representation of @f$ S^2 @f$. * Used for unit vectors on the sphere or directions in 3D. * * @todo add conversions from/to polar angles? */ template struct S2 { typedef _scalar scalar; typedef vect<3, scalar> vect_type; typedef SO3 SO3_type; typedef typename vect_type::base vec3; scalar length = scalar(den)/scalar(num); enum {DOF=2, TYP = 1, DIM = 3}; //private: /** * Unit vector on the sphere, or vector pointing in a direction */ vect_type vec; public: S2() { if(S2_typ == 3) vec=length * vec3(0, 0, std::sqrt(1)); if(S2_typ == 2) vec=length * vec3(0, std::sqrt(1), 0); if(S2_typ == 1) vec=length * vec3(std::sqrt(1), 0, 0); } S2(const scalar &x, const scalar &y, const scalar &z) : vec(vec3(x, y, z)) { vec.normalize(); vec = vec * length; } S2(const vect_type &_vec) : vec(_vec) { vec.normalize(); vec = vec * length; } void oplus(MTK::vectview delta, scalar scale = 1) { SO3_type res; res.w() = MTK::exp(res.vec(), delta, scalar(scale/2)); vec = res.toRotationMatrix() * vec; } void boxplus(MTK::vectview delta, scalar scale=1) { Eigen::Matrix Bx; S2_Bx(Bx); vect_type Bu = Bx*delta;SO3_type res; res.w() = MTK::exp(res.vec(), Bu, scalar(scale/2)); vec = res.toRotationMatrix() * vec; } void boxminus(MTK::vectview res, const S2& other) const { scalar v_sin = (MTK::hat(vec)*other.vec).norm(); scalar v_cos = vec.transpose() * other.vec; scalar theta = std::atan2(v_sin, v_cos); if(v_sin < MTK::tolerance()) { if(std::fabs(theta) > MTK::tolerance() ) { res[0] = 3.1415926; res[1] = 0; } else{ res[0] = 0; res[1] = 0; } } else { S2 other_copy = other; Eigen::MatrixBx; other_copy.S2_Bx(Bx); res = theta/v_sin * Bx.transpose() * MTK::hat(other.vec)*vec; } } void S2_hat(Eigen::Matrix &res) { Eigen::Matrix skew_vec; skew_vec << scalar(0), -vec[2], vec[1], vec[2], scalar(0), -vec[0], -vec[1], vec[0], scalar(0); res = skew_vec; } void S2_Bx(Eigen::Matrix &res) { if(S2_typ == 3) { if(vec[2] + length > tolerance()) { res << length - vec[0]*vec[0]/(length+vec[2]), -vec[0]*vec[1]/(length+vec[2]), -vec[0]*vec[1]/(length+vec[2]), length-vec[1]*vec[1]/(length+vec[2]), -vec[0], -vec[1]; res /= length; } else { res = Eigen::Matrix::Zero(); res(1, 1) = -1; res(2, 0) = 1; } } else if(S2_typ == 2) { if(vec[1] + length > tolerance()) { res << length - vec[0]*vec[0]/(length+vec[1]), -vec[0]*vec[2]/(length+vec[1]), -vec[0], -vec[2], -vec[0]*vec[2]/(length+vec[1]), length-vec[2]*vec[2]/(length+vec[1]); res /= length; } else { res = Eigen::Matrix::Zero(); res(1, 1) = -1; res(2, 0) = 1; } } else { if(vec[0] + length > tolerance()) { res << -vec[1], -vec[2], length - vec[1]*vec[1]/(length+vec[0]), -vec[2]*vec[1]/(length+vec[0]), -vec[2]*vec[1]/(length+vec[0]), length-vec[2]*vec[2]/(length+vec[0]); res /= length; } else { res = Eigen::Matrix::Zero(); res(1, 1) = -1; res(2, 0) = 1; } } } void S2_Nx(Eigen::Matrix &res, S2& subtrahend) { if((vec+subtrahend.vec).norm() > tolerance()) { Eigen::Matrix Bx; S2_Bx(Bx); if((vec-subtrahend.vec).norm() > tolerance()) { scalar v_sin = (MTK::hat(vec)*subtrahend.vec).norm(); scalar v_cos = vec.transpose() * subtrahend.vec; res = Bx.transpose() * (std::atan2(v_sin, v_cos)/v_sin*MTK::hat(vec)+MTK::hat(vec)*subtrahend.vec*((-v_cos/v_sin/v_sin/length/length/length/length+std::atan2(v_sin, v_cos)/v_sin/v_sin/v_sin)*subtrahend.vec.transpose()*MTK::hat(vec)*MTK::hat(vec)-vec.transpose()/length/length/length/length)); } else { res = 1/length/length*Bx.transpose()*MTK::hat(vec); } } else { std::cerr << "No N(x, y) for x=-y" << std::endl; std::exit(100); } } void S2_Nx_yy(Eigen::Matrix &res) { Eigen::Matrix Bx; S2_Bx(Bx); res = 1/length/length*Bx.transpose()*MTK::hat(vec); } void S2_Mx(Eigen::Matrix &res, MTK::vectview delta) { Eigen::Matrix Bx; S2_Bx(Bx); if(delta.norm() < tolerance()) { res = -MTK::hat(vec)*Bx; } else{ vect_type Bu = Bx*delta; SO3_type exp_delta; exp_delta.w() = MTK::exp(exp_delta.vec(), Bu, scalar(1/2)); res = -exp_delta.toRotationMatrix()*MTK::hat(vec)*MTK::A_matrix(Bu).transpose()*Bx; } } operator const vect_type&() const{ return vec; } const vect_type& get_vect() const { return vec; } friend S2 operator*(const SO3& rot, const S2& dir) { S2 ret; ret.vec = rot * dir.vec; return ret; } scalar operator[](int idx) const {return vec[idx]; } friend std::ostream& operator<<(std::ostream &os, const S2& vec){ return os << vec.vec.transpose() << " "; } friend std::istream& operator>>(std::istream &is, S2& vec){ for(int i=0; i<3; ++i) is >> vec.vec[i]; vec.vec.normalize(); vec.vec = vec.vec * vec.length; return is; } }; } // namespace MTK #endif /*S2_H_*/ ================================================ FILE: include/IKFoM_toolkit/mtk/types/SOn.hpp ================================================ // This is an advanced implementation of the algorithm described in the // following paper: // C. Hertzberg, R. Wagner, U. Frese, and L. Schroder. Integratinggeneric sensor fusion algorithms with sound state representationsthrough encapsulation of manifolds. // CoRR, vol. abs/1107.1119, 2011.[Online]. Available: http://arxiv.org/abs/1107.1119 /* * Copyright (c) 2019--2023, The University of Hong Kong * All rights reserved. * * Modifier: Dongjiao HE * * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions * are met: * * * Redistributions of source code must retain the above copyright * notice, this list of conditions and the following disclaimer. * * Redistributions in binary form must reproduce the above * copyright notice, this list of conditions and the following * disclaimer in the documentation and/or other materials provided * with the distribution. * * Neither the name of the Universitaet Bremen nor the names of its * contributors may be used to endorse or promote products derived * from this software without specific prior written permission. * * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE * POSSIBILITY OF SUCH DAMAGE. */ /* * Copyright (c) 2008--2011, Universitaet Bremen * All rights reserved. * * Author: Christoph Hertzberg * * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions * are met: * * * Redistributions of source code must retain the above copyright * notice, this list of conditions and the following disclaimer. * * Redistributions in binary form must reproduce the above * copyright notice, this list of conditions and the following * disclaimer in the documentation and/or other materials provided * with the distribution. * * Neither the name of the Universitaet Bremen nor the names of its * contributors may be used to endorse or promote products derived * from this software without specific prior written permission. * * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE * POSSIBILITY OF SUCH DAMAGE. */ /** * @file mtk/types/SOn.hpp * @brief Standard Orthogonal Groups i.e.\ rotatation groups. */ #ifndef SON_H_ #define SON_H_ #include #include "vect.hpp" #include "../src/mtkmath.hpp" namespace MTK { /** * Two-dimensional orientations represented as scalar. * There is no guarantee that the representing scalar is within any interval, * but the result of boxminus will always have magnitude @f$\le\pi @f$. */ template struct SO2 : public Eigen::Rotation2D<_scalar> { enum {DOF = 1, DIM = 2, TYP = 3}; typedef _scalar scalar; typedef Eigen::Rotation2D base; typedef vect vect_type; //! Construct from angle SO2(const scalar& angle = 0) : base(angle) { } //! Construct from Eigen::Rotation2D SO2(const base& src) : base(src) {} /** * Construct from 2D vector. * Resulting orientation will rotate the first unit vector to point to vec. */ SO2(const vect_type &vec) : base(atan2(vec[1], vec[0])) {}; //! Calculate @c this->inverse() * @c r SO2 operator%(const base &r) const { return base::inverse() * r; } //! Calculate @c this->inverse() * @c r template vect_type operator%(const Eigen::MatrixBase &vec) const { return base::inverse() * vec; } //! Calculate @c *this * @c r.inverse() SO2 operator/(const SO2 &r) const { return *this * r.inverse(); } //! Gets the angle as scalar. operator scalar() const { return base::angle(); } void S2_hat(Eigen::Matrix &res) { res = Eigen::Matrix::Zero(); } //! @name Manifold requirements void S2_Nx_yy(Eigen::Matrix &res) { std::cerr << "wrong idx for S2" << std::endl; std::exit(100); res = Eigen::Matrix::Zero(); } void S2_Mx(Eigen::Matrix &res, MTK::vectview delta) { std::cerr << "wrong idx for S2" << std::endl; std::exit(100); res = Eigen::Matrix::Zero(); } void oplus(MTK::vectview vec, scalar scale = 1) { base::angle() += scale * vec[0]; } void boxplus(MTK::vectview vec, scalar scale = 1) { base::angle() += scale * vec[0]; } void boxminus(MTK::vectview res, const SO2& other) const { res[0] = MTK::normalize(base::angle() - other.angle(), scalar(MTK::pi)); } friend std::istream& operator>>(std::istream &is, SO2& ang){ return is >> ang.angle(); } }; /** * Three-dimensional orientations represented as Quaternion. * It is assumed that the internal Quaternion always stays normalized, * should this not be the case, call inherited member function @c normalize(). */ template struct SO3 : public Eigen::Quaternion<_scalar, Options> { enum {DOF = 3, DIM = 3, TYP = 2}; typedef _scalar scalar; typedef Eigen::Quaternion base; typedef Eigen::Quaternion Quaternion; typedef vect vect_type; //! Calculate @c this->inverse() * @c r template EIGEN_STRONG_INLINE Quaternion operator%(const Eigen::QuaternionBase &r) const { return base::conjugate() * r; } //! Calculate @c this->inverse() * @c r template vect_type operator%(const Eigen::MatrixBase &vec) const { return base::conjugate() * vec; } //! Calculate @c this * @c r.conjugate() template EIGEN_STRONG_INLINE Quaternion operator/(const Eigen::QuaternionBase &r) const { return *this * r.conjugate(); } /** * Construct from real part and three imaginary parts. * Quaternion is normalized after construction. */ SO3(const scalar& w, const scalar& x, const scalar& y, const scalar& z) : base(w, x, y, z) { base::normalize(); } /** * Construct from Eigen::Quaternion. * @note Non-normalized input may result result in spurious behavior. */ SO3(const base& src = base::Identity()) : base(src) {} /** * Construct from rotation matrix. * @note Invalid rotation matrices may lead to spurious behavior. */ template SO3(const Eigen::MatrixBase& matrix) : base(matrix) {} /** * Construct from arbitrary rotation type. * @note Invalid rotation matrices may lead to spurious behavior. */ template SO3(const Eigen::RotationBase& rotation) : base(rotation.derived()) {} //! @name Manifold requirements void boxplus(MTK::vectview vec, scalar scale=1) { SO3 delta = exp(vec, scale); *this = *this * delta; } void boxminus(MTK::vectview res, const SO3& other) const { res = SO3::log(other.conjugate() * *this); } //} void oplus(MTK::vectview vec, scalar scale=1) { SO3 delta = exp(vec, scale); *this = *this * delta; } void S2_hat(Eigen::Matrix &res) { res = Eigen::Matrix::Zero(); } void S2_Nx_yy(Eigen::Matrix &res) { std::cerr << "wrong idx for S2" << std::endl; std::exit(100); res = Eigen::Matrix::Zero(); } void S2_Mx(Eigen::Matrix &res, MTK::vectview delta) { std::cerr << "wrong idx for S2" << std::endl; std::exit(100); res = Eigen::Matrix::Zero(); } friend std::ostream& operator<<(std::ostream &os, const SO3& q){ return os << q.coeffs().transpose() << " "; } friend std::istream& operator>>(std::istream &is, SO3& q){ vect<4,scalar> coeffs; is >> coeffs; q.coeffs() = coeffs.normalized(); return is; } //! @name Helper functions //{ /** * Calculate the exponential map. In matrix terms this would correspond * to the Rodrigues formula. */ // FIXME vectview<> can't be constructed from every MatrixBase<>, use const Vector3x& as workaround // static SO3 exp(MTK::vectview dvec, scalar scale = 1){ static SO3 exp(const Eigen::Matrix& dvec, scalar scale = 1){ SO3 res; res.w() = MTK::exp(res.vec(), dvec, scalar(scale/2)); return res; } /** * Calculate the inverse of @c exp. * Only guarantees that exp(log(x)) == x */ static typename base::Vector3 log(const SO3 &orient){ typename base::Vector3 res; MTK::log(res, orient.w(), orient.vec(), scalar(2), true); return res; } }; namespace internal { template struct UnalignedType >{ typedef SO2 type; }; template struct UnalignedType >{ typedef SO3 type; }; } // namespace internal } // namespace MTK #endif /*SON_H_*/ ================================================ FILE: include/IKFoM_toolkit/mtk/types/vect.hpp ================================================ // This is an advanced implementation of the algorithm described in the // following paper: // C. Hertzberg, R. Wagner, U. Frese, and L. Schroder. Integratinggeneric sensor fusion algorithms with sound state representationsthrough encapsulation of manifolds. // CoRR, vol. abs/1107.1119, 2011.[Online]. Available: http://arxiv.org/abs/1107.1119 /* * Copyright (c) 2019--2023, The University of Hong Kong * All rights reserved. * * Modifier: Dongjiao HE * * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions * are met: * * * Redistributions of source code must retain the above copyright * notice, this list of conditions and the following disclaimer. * * Redistributions in binary form must reproduce the above * copyright notice, this list of conditions and the following * disclaimer in the documentation and/or other materials provided * with the distribution. * * Neither the name of the Universitaet Bremen nor the names of its * contributors may be used to endorse or promote products derived * from this software without specific prior written permission. * * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE * POSSIBILITY OF SUCH DAMAGE. */ /* * Copyright (c) 2008--2011, Universitaet Bremen * All rights reserved. * * Author: Christoph Hertzberg * * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions * are met: * * * Redistributions of source code must retain the above copyright * notice, this list of conditions and the following disclaimer. * * Redistributions in binary form must reproduce the above * copyright notice, this list of conditions and the following * disclaimer in the documentation and/or other materials provided * with the distribution. * * Neither the name of the Universitaet Bremen nor the names of its * contributors may be used to endorse or promote products derived * from this software without specific prior written permission. * * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE * POSSIBILITY OF SUCH DAMAGE. */ /** * @file mtk/types/vect.hpp * @brief Basic vectors interpreted as manifolds. * * This file also implements a simple wrapper for matrices, for arbitrary scalars * and for positive scalars. */ #ifndef VECT_H_ #define VECT_H_ #include #include #include #include "../src/vectview.hpp" namespace MTK { static const Eigen::IOFormat IO_no_spaces(Eigen::StreamPrecision, Eigen::DontAlignCols, ",", ",", "", "", "[", "]"); /** * A simple vector class. * Implementation is basically a wrapper around Eigen::Matrix with manifold * requirements added. */ template struct vect : public Eigen::Matrix<_scalar, D, 1, _Options> { typedef Eigen::Matrix<_scalar, D, 1, _Options> base; enum {DOF = D, DIM = D, TYP = 0}; typedef _scalar scalar; //using base::operator=; /** Standard constructor. Sets all values to zero. */ vect(const base &src = base::Zero()) : base(src) {} /** Constructor copying the value of the expression \a other */ template EIGEN_STRONG_INLINE vect(const Eigen::DenseBase& other) : base(other) {} /** Construct from memory. */ vect(const scalar* src, int size = DOF) : base(base::Map(src, size)) { } void boxplus(MTK::vectview vec, scalar scale=1) { *this += scale * vec; } void boxminus(MTK::vectview res, const vect& other) const { res = *this - other; } void oplus(MTK::vectview vec, scalar scale=1) { *this += scale * vec; } void S2_hat(Eigen::Matrix &res) { res = Eigen::Matrix::Zero(); } void S2_Nx_yy(Eigen::Matrix &res) { std::cerr << "wrong idx for S2" << std::endl; std::exit(100); res = Eigen::Matrix::Zero(); } void S2_Mx(Eigen::Matrix &res, MTK::vectview delta) { std::cerr << "wrong idx for S2" << std::endl; std::exit(100); res = Eigen::Matrix::Zero(); } friend std::ostream& operator<<(std::ostream &os, const vect& v){ // Eigen sometimes messes with the streams flags, so output manually: for(int i=0; i>(std::istream &is, vect& v){ char term=0; is >> std::ws; // skip whitespace switch(is.peek()) { case '(': term=')'; is.ignore(1); break; case '[': term=']'; is.ignore(1); break; case '{': term='}'; is.ignore(1); break; default: break; } if(D==Eigen::Dynamic) { assert(term !=0 && "Dynamic vectors must be embraced"); std::vector temp; while(is.good() && is.peek() != term) { scalar x; is >> x; temp.push_back(x); if(is.peek()==',') is.ignore(1); } v = vect::Map(temp.data(), temp.size()); } else for(int i=0; i> v[i]; if(is.peek()==',') { // ignore commas between values is.ignore(1); } } if(term!=0) { char x; is >> x; if(x!=term) { is.setstate(is.badbit); // assert(x==term && "start and end bracket do not match!"); } } return is; } template vectview tail(){ BOOST_STATIC_ASSERT(0< dim && dim <= DOF); return base::template tail(); } template vectview tail() const{ BOOST_STATIC_ASSERT(0< dim && dim <= DOF); return base::template tail(); } template vectview head(){ BOOST_STATIC_ASSERT(0< dim && dim <= DOF); return base::template head(); } template vectview head() const{ BOOST_STATIC_ASSERT(0< dim && dim <= DOF); return base::template head(); } }; /** * A simple matrix class. * Implementation is basically a wrapper around Eigen::Matrix with manifold * requirements added, i.e., matrix is viewed as a plain vector for that. */ template::Options> struct matrix : public Eigen::Matrix<_scalar, M, N, _Options> { typedef Eigen::Matrix<_scalar, M, N, _Options> base; enum {DOF = M * N, TYP = 4, DIM=0}; typedef _scalar scalar; using base::operator=; /** Standard constructor. Sets all values to zero. */ matrix() { base::setZero(); } /** Constructor copying the value of the expression \a other */ template EIGEN_STRONG_INLINE matrix(const Eigen::MatrixBase& other) : base(other) {} /** Construct from memory. */ matrix(const scalar* src) : base(src) { } void boxplus(MTK::vectview vec, scalar scale = 1) { *this += scale * base::Map(vec.data()); } void boxminus(MTK::vectview res, const matrix& other) const { base::Map(res.data()) = *this - other; } void S2_hat(Eigen::Matrix &res) { res = Eigen::Matrix::Zero(); } void oplus(MTK::vectview vec, scalar scale = 1) { *this += scale * base::Map(vec.data()); } void S2_Nx_yy(Eigen::Matrix &res) { std::cerr << "wrong idx for S2" << std::endl; std::exit(100); res = Eigen::Matrix::Zero(); } void S2_Mx(Eigen::Matrix &res, MTK::vectview delta) { std::cerr << "wrong idx for S2" << std::endl; std::exit(100); res = Eigen::Matrix::Zero(); } friend std::ostream& operator<<(std::ostream &os, const matrix& mat){ for(int i=0; i>(std::istream &is, matrix& mat){ for(int i=0; i> mat.data()[i]; } return is; } };// @todo What if M / N = Eigen::Dynamic? /** * A simple scalar type. */ template struct Scalar { enum {DOF = 1, TYP = 5, DIM=0}; typedef _scalar scalar; scalar value; Scalar(const scalar& value = scalar(0)) : value(value) {} operator const scalar&() const { return value; } operator scalar&() { return value; } Scalar& operator=(const scalar& val) { value = val; return *this; } void S2_hat(Eigen::Matrix &res) { res = Eigen::Matrix::Zero(); } void S2_Nx_yy(Eigen::Matrix &res) { std::cerr << "wrong idx for S2" << std::endl; std::exit(100); res = Eigen::Matrix::Zero(); } void S2_Mx(Eigen::Matrix &res, MTK::vectview delta) { std::cerr << "wrong idx for S2" << std::endl; std::exit(100); res = Eigen::Matrix::Zero(); } void oplus(MTK::vectview vec, scalar scale=1) { value += scale * vec[0]; } void boxplus(MTK::vectview vec, scalar scale=1) { value += scale * vec[0]; } void boxminus(MTK::vectview res, const Scalar& other) const { res[0] = *this - other; } }; /** * Positive scalars. * Boxplus is implemented using multiplication by @f$x\boxplus\delta = x\cdot\exp(\delta) @f$. */ template struct PositiveScalar { enum {DOF = 1, TYP = 6, DIM=0}; typedef _scalar scalar; scalar value; PositiveScalar(const scalar& value = scalar(1)) : value(value) { assert(value > scalar(0)); } operator const scalar&() const { return value; } PositiveScalar& operator=(const scalar& val) { assert(val>0); value = val; return *this; } void boxplus(MTK::vectview vec, scalar scale = 1) { value *= std::exp(scale * vec[0]); } void boxminus(MTK::vectview res, const PositiveScalar& other) const { res[0] = std::log(*this / other); } void oplus(MTK::vectview vec, scalar scale = 1) { value *= std::exp(scale * vec[0]); } void S2_hat(Eigen::Matrix &res) { res = Eigen::Matrix::Zero(); } void S2_Nx_yy(Eigen::Matrix &res) { std::cerr << "wrong idx for S2" << std::endl; std::exit(100); res = Eigen::Matrix::Zero(); } void S2_Mx(Eigen::Matrix &res, MTK::vectview delta) { std::cerr << "wrong idx for S2" << std::endl; std::exit(100); res = Eigen::Matrix::Zero(); } friend std::istream& operator>>(std::istream &is, PositiveScalar& s){ is >> s.value; assert(s.value > 0); return is; } }; template struct Complex : public std::complex<_scalar>{ enum {DOF = 2, TYP = 7, DIM=0}; typedef _scalar scalar; typedef std::complex Base; Complex(const Base& value) : Base(value) {} Complex(const scalar& re = 0.0, const scalar& im = 0.0) : Base(re, im) {} Complex(const MTK::vectview &in) : Base(in[0], in[1]) {} template Complex(const Eigen::DenseBase &in) : Base(in[0], in[1]) {} void boxplus(MTK::vectview vec, scalar scale = 1) { Base::real() += scale * vec[0]; Base::imag() += scale * vec[1]; }; void boxminus(MTK::vectview res, const Complex& other) const { Complex diff = *this - other; res << diff.real(), diff.imag(); } void S2_hat(Eigen::Matrix &res) { res = Eigen::Matrix::Zero(); } void oplus(MTK::vectview vec, scalar scale = 1) { Base::real() += scale * vec[0]; Base::imag() += scale * vec[1]; }; void S2_Nx_yy(Eigen::Matrix &res) { std::cerr << "wrong idx for S2" << std::endl; std::exit(100); res = Eigen::Matrix::Zero(); } void S2_Mx(Eigen::Matrix &res, MTK::vectview delta) { std::cerr << "wrong idx for S2" << std::endl; std::exit(100); res = Eigen::Matrix::Zero(); } scalar squaredNorm() const { return std::pow(Base::real(),2) + std::pow(Base::imag(),2); } const scalar& operator()(int i) const { assert(0<=i && i<2 && "Index out of range"); return i==0 ? Base::real() : Base::imag(); } scalar& operator()(int i){ assert(0<=i && i<2 && "Index out of range"); return i==0 ? Base::real() : Base::imag(); } }; namespace internal { template struct UnalignedType >{ typedef vect type; }; } // namespace internal } // namespace MTK #endif /*VECT_H_*/ ================================================ FILE: include/IKFoM_toolkit/mtk/types/wrapped_cv_mat.hpp ================================================ /* * Copyright (c) 2010--2011, Universitaet Bremen and DFKI GmbH * All rights reserved. * * Author: Rene Wagner * Christoph Hertzberg * * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions * are met: * * * Redistributions of source code must retain the above copyright * notice, this list of conditions and the following disclaimer. * * Redistributions in binary form must reproduce the above * copyright notice, this list of conditions and the following * disclaimer in the documentation and/or other materials provided * with the distribution. * * Neither the name of the Universitaet Bremen nor the DFKI GmbH * nor the names of its contributors may be used to endorse or * promote products derived from this software without specific * prior written permission. * * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE * POSSIBILITY OF SUCH DAMAGE. */ #ifndef WRAPPED_CV_MAT_HPP_ #define WRAPPED_CV_MAT_HPP_ #include #include namespace MTK { template struct cv_f_type; template<> struct cv_f_type { enum {value = CV_64F}; }; template<> struct cv_f_type { enum {value = CV_32F}; }; /** * cv_mat wraps a CvMat around an Eigen Matrix */ template class cv_mat : public matrix { typedef matrix base_type; enum {type_ = cv_f_type::value}; CvMat cv_mat_; public: cv_mat() { cv_mat_ = cvMat(rows, cols, type_, base_type::data()); } cv_mat(const cv_mat& oth) : base_type(oth) { cv_mat_ = cvMat(rows, cols, type_, base_type::data()); } template cv_mat(const Eigen::MatrixBase &value) : base_type(value) { cv_mat_ = cvMat(rows, cols, type_, base_type::data()); } template cv_mat& operator=(const Eigen::MatrixBase &value) { base_type::operator=(value); return *this; } cv_mat& operator=(const cv_mat& value) { base_type::operator=(value); return *this; } // FIXME: Maybe overloading operator& is not a good idea ... CvMat* operator&() { return &cv_mat_; } const CvMat* operator&() const { return &cv_mat_; } }; } // namespace MTK #endif /* WRAPPED_CV_MAT_HPP_ */ ================================================ FILE: include/common_lib.h ================================================ #ifndef COMMON_LIB_H #define COMMON_LIB_H #include #include #include #include #include #include #include #include #include #include "point_type.h" using namespace std; using namespace Eigen; #define USE_IKFOM #define PI_M (3.14159265358) #define G_m_s2 (9.81) // Gravaty const in GuangDong/China #define DIM_STATE (18) // Dimension of states (Let Dim(SO(3)) = 3) #define DIM_PROC_N (12) // Dimension of process noise (Let Dim(SO(3)) = 3) #define CUBE_LEN (6.0) #define LIDAR_SP_LEN (2) #define INIT_COV (1) #define NUM_MATCH_POINTS (5) #define MAX_MEAS_DIM (10000) #define VEC_FROM_ARRAY(v) v[0],v[1],v[2] #define MAT_FROM_ARRAY(v) v[0],v[1],v[2],v[3],v[4],v[5],v[6],v[7],v[8] #define CONSTRAIN(v,min,max) ((v>min)?((v (mat.data(), mat.data() + mat.rows() * mat.cols()) #define DEBUG_FILE_DIR(name) (string(string(ROOT_DIR) + "Log/"+ name)) typedef log_lio::Pose6D Pose6D; typedef pcl::PointXYZINormal PointType; //typedef ikdTree_PointType PointType; typedef pcl::PointCloud PointCloudXYZI; typedef vector> PointVector; typedef Vector3d V3D; typedef Matrix3d M3D; typedef Vector3f V3F; typedef Matrix3f M3F; #define MD(a,b) Matrix #define VD(a) Matrix #define MF(a,b) Matrix #define VF(a) Matrix M3D Eye3d(M3D::Identity()); M3F Eye3f(M3F::Identity()); V3D Zero3d(0, 0, 0); V3F Zero3f(0, 0, 0); struct MeasureGroup // Lidar data and imu dates for the curent process { MeasureGroup() { lidar_beg_time = 0.0; this->lidar.reset(new PointCloudXYZI()); }; double lidar_beg_time; double lidar_end_time; PointCloudXYZI::Ptr lidar; deque imu; }; struct StatesGroup { StatesGroup() { this->rot_end = M3D::Identity(); this->pos_end = Zero3d; this->vel_end = Zero3d; this->bias_g = Zero3d; this->bias_a = Zero3d; this->gravity = Zero3d; this->cov = MD(DIM_STATE,DIM_STATE)::Identity() * INIT_COV; this->cov.block<9,9>(9,9) = MD(9,9)::Identity() * 0.00001; }; StatesGroup(const StatesGroup& b) { this->rot_end = b.rot_end; this->pos_end = b.pos_end; this->vel_end = b.vel_end; this->bias_g = b.bias_g; this->bias_a = b.bias_a; this->gravity = b.gravity; this->cov = b.cov; }; StatesGroup& operator=(const StatesGroup& b) { this->rot_end = b.rot_end; this->pos_end = b.pos_end; this->vel_end = b.vel_end; this->bias_g = b.bias_g; this->bias_a = b.bias_a; this->gravity = b.gravity; this->cov = b.cov; return *this; }; StatesGroup operator+(const Matrix &state_add) { StatesGroup a; a.rot_end = this->rot_end * Exp(state_add(0,0), state_add(1,0), state_add(2,0)); a.pos_end = this->pos_end + state_add.block<3,1>(3,0); a.vel_end = this->vel_end + state_add.block<3,1>(6,0); a.bias_g = this->bias_g + state_add.block<3,1>(9,0); a.bias_a = this->bias_a + state_add.block<3,1>(12,0); a.gravity = this->gravity + state_add.block<3,1>(15,0); a.cov = this->cov; return a; }; StatesGroup& operator+=(const Matrix &state_add) { this->rot_end = this->rot_end * Exp(state_add(0,0), state_add(1,0), state_add(2,0)); this->pos_end += state_add.block<3,1>(3,0); this->vel_end += state_add.block<3,1>(6,0); this->bias_g += state_add.block<3,1>(9,0); this->bias_a += state_add.block<3,1>(12,0); this->gravity += state_add.block<3,1>(15,0); return *this; }; Matrix operator-(const StatesGroup& b) { Matrix a; M3D rotd(b.rot_end.transpose() * this->rot_end); a.block<3,1>(0,0) = Log(rotd); a.block<3,1>(3,0) = this->pos_end - b.pos_end; a.block<3,1>(6,0) = this->vel_end - b.vel_end; a.block<3,1>(9,0) = this->bias_g - b.bias_g; a.block<3,1>(12,0) = this->bias_a - b.bias_a; a.block<3,1>(15,0) = this->gravity - b.gravity; return a; }; void resetpose() { this->rot_end = M3D::Identity(); this->pos_end = Zero3d; this->vel_end = Zero3d; } M3D rot_end; // the estimated attitude (rotation matrix) at the end lidar point V3D pos_end; // the estimated position at the end lidar point (world frame) V3D vel_end; // the estimated velocity at the end lidar point (world frame) V3D bias_g; // gyroscope bias V3D bias_a; // accelerator bias V3D gravity; // the estimated gravity acceleration Matrix cov; // states covariance }; template T rad2deg(T radians) { return radians * 180.0 / PI_M; } template T deg2rad(T degrees) { return degrees * PI_M / 180.0; } template auto set_pose6d(const double t, const Matrix &a, const Matrix &g, \ const Matrix &v, const Matrix &p, const Matrix &R) { Pose6D rot_kp; rot_kp.offset_time = t; for (int i = 0; i < 3; i++) { rot_kp.acc[i] = a(i); rot_kp.gyr[i] = g(i); rot_kp.vel[i] = v(i); rot_kp.pos[i] = p(i); for (int j = 0; j < 3; j++) rot_kp.rot[i*3+j] = R(i,j); } return move(rot_kp); } /* comment plane equation: Ax + By + Cz + D = 0 convert to: A/D*x + B/D*y + C/D*z = -1 solve: A0*x0 = b0 where A0_i = [x_i, y_i, z_i], x0 = [A/D, B/D, C/D]^T, b0 = [-1, ..., -1]^T normvec: normalized x0 */ template bool esti_normvector(Matrix &normvec, const PointVector &point, const T &threshold, const int &point_num) { MatrixXf A(point_num, 3); MatrixXf b(point_num, 1); b.setOnes(); b *= -1.0f; for (int j = 0; j < point_num; j++) { A(j,0) = point[j].x; A(j,1) = point[j].y; A(j,2) = point[j].z; } normvec = A.colPivHouseholderQr().solve(b); for (int j = 0; j < point_num; j++) { if (fabs(normvec(0) * point[j].x + normvec(1) * point[j].y + normvec(2) * point[j].z + 1.0f) > threshold) { return false; } } normvec.normalize(); return true; } float calc_dist(PointType p1, PointType p2){ float d = (p1.x - p2.x) * (p1.x - p2.x) + (p1.y - p2.y) * (p1.y - p2.y) + (p1.z - p2.z) * (p1.z - p2.z); return d; } template bool esti_plane(Matrix &pca_result, const PointVector &point, const T &threshold) { Matrix A; Matrix b; A.setZero(); b.setOnes(); b *= -1.0f; for (int j = 0; j < NUM_MATCH_POINTS; j++) { A(j,0) = point[j].x; A(j,1) = point[j].y; A(j,2) = point[j].z; } Matrix normvec = A.colPivHouseholderQr().solve(b); // n * p + b = 0 T n = normvec.norm(); pca_result(0) = normvec(0) / n; pca_result(1) = normvec(1) / n; pca_result(2) = normvec(2) / n; pca_result(3) = 1.0 / n; for (int j = 0; j < NUM_MATCH_POINTS; j++) { if (fabs(pca_result(0) * point[j].x + pca_result(1) * point[j].y + pca_result(2) * point[j].z + pca_result(3)) > threshold) { return false; } } return true; } #endif ================================================ FILE: include/ikd-Tree/ikd_Tree.cpp ================================================ #include "ikd_Tree.h" //#include //#include "voxel_map_util.hpp" /* Description: ikd-Tree: an incremental k-d tree for robotic applications Author: Yixi Cai email: yixicai@connect.hku.hk */ extern float mid2min, planarity, angle_threshold; extern int num_surfel, surfel_points_min; const float flag_surfel = -9999; VoxelInfo::VoxelInfo(const PointType &p) { // pthread_mutex_init(&updating_mutex_lock, NULL); // pthread_mutex_lock(&updating_mutex_lock); flag_updating = true; s_xx = p.x * p.x; s_xy = p.x * p.y; s_xz = p.x * p.z; s_yy = p.y * p.y; s_yz = p.y * p.z; s_zz = p.z * p.z; num_points = 1; mean_x = p.x; mean_y = p.y; mean_z = p.z; normal = p.getNormalVector3fMap(); // cube_center(0) = voxel_center[0]; // cube_center(1) = voxel_center[1]; // cube_center(2) = voxel_center[2]; points.push_back(p); is_init = true; plane = new Plane; flag_updating = false; // pthread_mutex_unlock(&updating_mutex_lock); } void VoxelInfo::getSimpInfo(SimpInfo& out) { out.s_xx = s_xx; out.s_yy = s_yy; out.s_zz = s_zz; out.s_xy = s_xy; out.s_xz = s_xz; out.s_yz = s_yz; out.normal = normal; out.mean_xyz(0) = mean_x; out.mean_xyz(1) = mean_y; out.mean_xyz(2) = mean_z; } void VoxelInfo::init() { num_points = 0; s_xx = 0.0; s_xy = 0.0; s_xz = 0.0; s_yy = 0.0; s_yz = 0.0; s_zz = 0.0; mean_x = 0.0; mean_y = 0.0; mean_z = 0.0; normal = Eigen::Vector3f::Zero(); plane = new Plane; is_init = true; } void VoxelInfo::updateWithSingle(const PointType &p, const Eigen::Matrix3f & cov) { // pthread_mutex_lock(&updating_mutex_lock); // lock_guard lock(updating_mutex_lock); flag_updating = true; updateSumAndMean(p); points.push_back(p); updateNormal(p); //check convergence if (num_points >= surfel_points_min) { checkPCA(); // double t1 = omp_get_wtime(); updatePlaneUncertainty(p, cov); // t1 = omp_get_wtime() - t1; // printf("updatePlaneUncertainty cost: %fs\n", t1); } flag_updating = false; // pthread_mutex_unlock(&updating_mutex_lock); } void VoxelInfo::updateWithSingle(const PointType &p) { // pthread_mutex_lock(&updating_mutex_lock); // lock_guard lock(updating_mutex_lock); flag_updating = true; points.push_back(p); updateSumAndMean(p); updateNormal(p); if (num_points >= surfel_points_min) checkPCA(); flag_updating = false; // pthread_mutex_unlock(&updating_mutex_lock); } void VoxelInfo::updateSumAndMean(const PointType & p) { // num of points in the voxel ++(num_points); // summation of p * p^t s_xx += p.x * p.x; s_xy += p.x * p.y; s_xz += p.x * p.z; s_yy += p.y * p.y; s_yz += p.y * p.z; s_zz += p.z * p.z; // summation of p float n = (float)num_points; mean_x = mean_x * (n - 1.0) / n + p.x / n; mean_y = mean_y * (n - 1.0) / n + p.y / n; mean_z = mean_z * (n - 1.0) / n + p.z / n; } void VoxelInfo::updatePlaneUncertainty(const PointType &p, const Eigen::Matrix3f & p_cov) { plane->plane_cov = Eigen::Matrix::Zero(); plane->points_size = points.size(); // plane covariance calculation Eigen::Matrix3f J_Q; // dq/dp, q = mean_p J_Q << 1.0 / plane->points_size, 0, 0, 0, 1.0 / plane->points_size, 0, 0, 0, 1.0 / plane->points_size; //diag(1/n, 1/n, 1/n) // min eigen value small enough // if (evalsReal(evalsMin) < planer_threshold_) { if (num_points == flag_surfel) { vector index(points.size()); Eigen::Matrix3f U; // eigen vector U.block<3, 1>(0, 0) = plane->x_normal; U.block<3, 1>(0, 1) = plane->y_normal; U.block<3, 1>(0, 2) = plane->normal; // traverse all points for (int i = 0; i < points.size(); i++) { Eigen::Matrix J; // 6 * 3 Eigen::Matrix3f F; F.row(0) = (points[i].getVector3fMap() - plane->center).transpose() / // (p - mean_p)^T / ((plane->points_size) * (plane->min_eigen_value - plane->max_eigen_value)) * // n * (lambda_3 - lambda_m) * (plane->x_normal * plane->normal.transpose() + // (u_m * n^T + n^T * u_m^T) plane->normal * plane->x_normal.transpose()); F.row(1) = (points[i].getVector3fMap() - plane->center).transpose() / // (p - mean_p)^T / ((plane->points_size) * (plane->min_eigen_value - plane->mid_eigen_value)) * // n * (lambda_3 - lambda_m) * (plane->y_normal * plane->normal.transpose() + // (u_m * n^T + n^T * u_m^T) plane->normal * plane->y_normal.transpose()); F.row(2) << 0, 0, 0; J.block<3, 3>(0, 0) = U * F; // U * F, (dn/dp) J.block<3, 3>(3, 0) = J_Q; //J_Q, (dq/dp), q = mean_p plane->plane_cov += J * p_cov * J.transpose(); //cov (n, q), q = mean_p } plane->is_plane = true; if (plane->last_update_points_size == 0) { plane->last_update_points_size = plane->points_size; plane->is_update = true; } else if (plane->points_size - plane->last_update_points_size > 100) { // update interval: 100 points plane->last_update_points_size = plane->points_size; plane->is_update = true; } if (!plane->is_init) { plane->id = plane_id; plane_id++; plane->is_init = true; } } else { // min eigen value is not small enough, not yet a plane if (!plane->is_init) { plane->id = plane_id; plane_id++; plane->is_init = true; } if (plane->last_update_points_size == 0) { plane->last_update_points_size = plane->points_size; plane->is_update = true; } else if (plane->points_size - plane->last_update_points_size > 100) { // update interval: 100 points plane->last_update_points_size = plane->points_size; plane->is_update = true; } plane->is_plane = false; } } void VoxelInfo::updateNormal(const PointType &p) { // downsample normal normal = normal * (num_points - 1.0) / (float)num_points + p.getNormalVector3fMap() / (float)num_points; normal.normalize(); } bool VoxelInfo::checkPCA() { const float n = (float)num_points; Eigen::Matrix3f cov; cov(0, 0) = s_xx / n - mean_x * mean_x; cov(1, 1) = s_yy / n - mean_y * mean_y; cov(2, 2) = s_zz / n - mean_z * mean_z; cov(0, 1) = s_xy / n - mean_x * mean_y; cov(1, 0) = cov(0, 1); cov(0, 2) = s_xz / n - mean_x * mean_z; cov(2, 0) = cov(0, 2); cov(1, 2) = s_yz / n - mean_y * mean_z; cov(2, 1) = cov(1, 2); Eigen::Matrix3f eigenvectors_; Eigen::Vector3f eigenvalues_; // Compute eigen vectors and values Eigen::SelfAdjointEigenSolver evd (cov); // Organize eigenvectors and eigenvalues in ascendent order // lambda 1 > lambda 2 > lambda 3 for (int i = 0; i < 3; ++i) { eigenvalues_[i] = evd.eigenvalues () [2-i]; eigenvectors_.col (i) = evd.eigenvectors ().col (2-i); } float normal_dot = eigenvectors_.col (2).dot(normal); float angle = acos(normal_dot); // 0 ~ pi angle = angle / M_PI * 180.0; if (normal_dot < 0) { eigenvectors_.col(2) *= -1.0; angle = 180.0 - angle; } ///planarity float pho = 2.0 * (eigenvalues_[1] - eigenvalues_[2]) / (eigenvalues_[0] + eigenvalues_[1] + eigenvalues_[2]); float mid_min = eigenvalues_[1] / eigenvalues_[2]; if (mid_min > mid2min && pho > planarity) { if (num_points > 0) { num_points = flag_surfel; // mark surfel ++num_surfel; } if (angle < angle_threshold) { normal = (normal + eigenvectors_.col (2)) / 2.0; //todo normal.normalize(); } else normal = eigenvectors_.col (2); //record parameters for plane plane->covariance = cov; plane->center << mean_x, mean_y, mean_z; plane->normal << eigenvectors_.col (2); plane->y_normal << eigenvectors_.col (1); plane->x_normal << eigenvectors_.col (0); plane->min_eigen_value = eigenvalues_[2]; plane->mid_eigen_value = eigenvalues_[1]; plane->max_eigen_value = eigenvalues_[0]; plane->radius = plane->max_eigen_value; plane->d = -plane->normal.dot(plane->center); return true; } else return false; } template KD_TREE::KD_TREE(float delete_param, float balance_param, float box_length) { delete_criterion_param = delete_param; balance_criterion_param = balance_param; downsample_size = box_length; Rebuild_Logger.clear(); termination_flag = false; start_thread(); } template KD_TREE::~KD_TREE() { stop_thread(); Delete_Storage_Disabled = true; delete_tree_nodes(&Root_Node); PointVector ().swap(PCL_Storage); Rebuild_Logger.clear(); } template void KD_TREE::Set_delete_criterion_param(float delete_param){ delete_criterion_param = delete_param; } template void KD_TREE::Set_balance_criterion_param(float balance_param){ balance_criterion_param = balance_param; } template void KD_TREE::set_downsample_param(float downsample_param){ downsample_size = downsample_param; half_voxel_size = 0.5 * downsample_size; quater_voxel_size = 0.5 * half_voxel_size; printf("half_voxel_size: %0.6f, quater_voxel_size: %0.6f\n", half_voxel_size, quater_voxel_size); } template void KD_TREE::InitializeKDTree(float delete_param, float balance_param, float box_length){ Set_delete_criterion_param(delete_param); Set_balance_criterion_param(balance_param); set_downsample_param(box_length); } template void KD_TREE::InitTreeNode(KD_TREE_NODE * root){ root->point.x = 0.0f; root->point.y = 0.0f; root->point.z = 0.0f; root->point.normal_x = 0.0f; root->point.normal_y = 0.0f; root->point.normal_z = 0.0f; root->node_range_x[0] = 0.0f; root->node_range_x[1] = 0.0f; root->node_range_y[0] = 0.0f; root->node_range_y[1] = 0.0f; root->node_range_z[0] = 0.0f; root->node_range_z[1] = 0.0f; root->division_axis = 0; root->father_ptr = nullptr; root->left_son_ptr = nullptr; root->right_son_ptr = nullptr; root->TreeSize = 0; root->invalid_point_num = 0; root->down_del_num = 0; root->point_deleted = false; root->tree_deleted = false; root->need_push_down_to_left = false; root->need_push_down_to_right = false; root->point_downsample_deleted = false; root->working_flag = false; pthread_mutex_init(&(root->push_down_mutex_lock),NULL); } template int KD_TREE::size(){ int s = 0; if (Rebuild_Ptr == nullptr || *Rebuild_Ptr != Root_Node){ if (Root_Node != nullptr) { return Root_Node->TreeSize; } else { return 0; } } else { if (!pthread_mutex_trylock(&working_flag_mutex)){ s = Root_Node->TreeSize; pthread_mutex_unlock(&working_flag_mutex); return s; } else { return Treesize_tmp; } } } template BoxPointType KD_TREE::tree_range(){ BoxPointType range; if (Rebuild_Ptr == nullptr || *Rebuild_Ptr != Root_Node){ if (Root_Node != nullptr) { range.vertex_min[0] = Root_Node->node_range_x[0]; range.vertex_min[1] = Root_Node->node_range_y[0]; range.vertex_min[2] = Root_Node->node_range_z[0]; range.vertex_max[0] = Root_Node->node_range_x[1]; range.vertex_max[1] = Root_Node->node_range_y[1]; range.vertex_max[2] = Root_Node->node_range_z[1]; } else { memset(&range, 0, sizeof(range)); } } else { if (!pthread_mutex_trylock(&working_flag_mutex)){ range.vertex_min[0] = Root_Node->node_range_x[0]; range.vertex_min[1] = Root_Node->node_range_y[0]; range.vertex_min[2] = Root_Node->node_range_z[0]; range.vertex_max[0] = Root_Node->node_range_x[1]; range.vertex_max[1] = Root_Node->node_range_y[1]; range.vertex_max[2] = Root_Node->node_range_z[1]; pthread_mutex_unlock(&working_flag_mutex); } else { memset(&range, 0, sizeof(range)); } } return range; } template int KD_TREE::validnum(){ int s = 0; if (Rebuild_Ptr == nullptr || *Rebuild_Ptr != Root_Node){ if (Root_Node != nullptr) return (Root_Node->TreeSize - Root_Node->invalid_point_num); else return 0; } else { if (!pthread_mutex_trylock(&working_flag_mutex)){ s = Root_Node->TreeSize-Root_Node->invalid_point_num; pthread_mutex_unlock(&working_flag_mutex); return s; } else { return -1; } } } template void KD_TREE::root_alpha(float &alpha_bal, float &alpha_del){ if (Rebuild_Ptr == nullptr || *Rebuild_Ptr != Root_Node){ alpha_bal = Root_Node->alpha_bal; alpha_del = Root_Node->alpha_del; return; } else { if (!pthread_mutex_trylock(&working_flag_mutex)){ alpha_bal = Root_Node->alpha_bal; alpha_del = Root_Node->alpha_del; pthread_mutex_unlock(&working_flag_mutex); return; } else { alpha_bal = alpha_bal_tmp; alpha_del = alpha_del_tmp; return; } } } template void KD_TREE::start_thread(){ pthread_mutex_init(&termination_flag_mutex_lock, NULL); pthread_mutex_init(&rebuild_ptr_mutex_lock, NULL); pthread_mutex_init(&rebuild_logger_mutex_lock, NULL); pthread_mutex_init(&points_deleted_rebuild_mutex_lock, NULL); pthread_mutex_init(&working_flag_mutex, NULL); pthread_mutex_init(&search_flag_mutex, NULL); pthread_create(&rebuild_thread, NULL, multi_thread_ptr, (void*) this); printf("Multi thread started \n"); } template void KD_TREE::stop_thread(){ pthread_mutex_lock(&termination_flag_mutex_lock); termination_flag = true; pthread_mutex_unlock(&termination_flag_mutex_lock); if (rebuild_thread) pthread_join(rebuild_thread, NULL); pthread_mutex_destroy(&termination_flag_mutex_lock); pthread_mutex_destroy(&rebuild_logger_mutex_lock); pthread_mutex_destroy(&rebuild_ptr_mutex_lock); pthread_mutex_destroy(&points_deleted_rebuild_mutex_lock); pthread_mutex_destroy(&working_flag_mutex); pthread_mutex_destroy(&search_flag_mutex); } template void * KD_TREE::multi_thread_ptr(void * arg){ KD_TREE * handle = (KD_TREE*) arg; handle->multi_thread_rebuild(); return nullptr; } template void KD_TREE::multi_thread_rebuild(){ bool terminated = false; KD_TREE_NODE * father_ptr, ** new_node_ptr; pthread_mutex_lock(&termination_flag_mutex_lock); terminated = termination_flag; pthread_mutex_unlock(&termination_flag_mutex_lock); while (!terminated){ pthread_mutex_lock(&rebuild_ptr_mutex_lock); pthread_mutex_lock(&working_flag_mutex); if (Rebuild_Ptr != nullptr ){ /* Traverse and copy */ if (!Rebuild_Logger.empty()){ printf("\n\n\n\n\n\n\n\n\n\n\n ERROR!!! \n\n\n\n\n\n\n\n\n"); } rebuild_flag = true; if (*Rebuild_Ptr == Root_Node) { Treesize_tmp = Root_Node->TreeSize; Validnum_tmp = Root_Node->TreeSize - Root_Node->invalid_point_num; alpha_bal_tmp = Root_Node->alpha_bal; alpha_del_tmp = Root_Node->alpha_del; } KD_TREE_NODE * old_root_node = (*Rebuild_Ptr); father_ptr = (*Rebuild_Ptr)->father_ptr; PointVector ().swap(Rebuild_PCL_Storage); // Lock Search pthread_mutex_lock(&search_flag_mutex); while (search_mutex_counter != 0){ pthread_mutex_unlock(&search_flag_mutex); usleep(1); pthread_mutex_lock(&search_flag_mutex); } search_mutex_counter = -1; //标记-1,执行flatten pthread_mutex_unlock(&search_flag_mutex); // Lock deleted points cache pthread_mutex_lock(&points_deleted_rebuild_mutex_lock); flatten(*Rebuild_Ptr, Rebuild_PCL_Storage, MULTI_THREAD_REC); /// // Unlock deleted points cache pthread_mutex_unlock(&points_deleted_rebuild_mutex_lock); // Unlock Search pthread_mutex_lock(&search_flag_mutex); //search_flag_mutex上锁 search_mutex_counter = 0;//标记为0 pthread_mutex_unlock(&search_flag_mutex); pthread_mutex_unlock(&working_flag_mutex); /* Rebuild and update missed operations*/ Operation_Logger_Type Operation; KD_TREE_NODE * new_root_node = nullptr; if (int(Rebuild_PCL_Storage.size()) > 0){ BuildTree(&new_root_node, 0, Rebuild_PCL_Storage.size()-1, Rebuild_PCL_Storage); // Rebuild has been done. Updates the blocked operations into the new tree pthread_mutex_lock(&working_flag_mutex); pthread_mutex_lock(&rebuild_logger_mutex_lock); int tmp_counter = 0; while (!Rebuild_Logger.empty()){ Operation = Rebuild_Logger.front(); max_queue_size = max(max_queue_size, Rebuild_Logger.size()); Rebuild_Logger.pop(); pthread_mutex_unlock(&rebuild_logger_mutex_lock); pthread_mutex_unlock(&working_flag_mutex); run_operation(&new_root_node, Operation); tmp_counter ++; if (tmp_counter % 10 == 0) usleep(1); pthread_mutex_lock(&working_flag_mutex); pthread_mutex_lock(&rebuild_logger_mutex_lock); } pthread_mutex_unlock(&rebuild_logger_mutex_lock); } /* Replace to original tree*/ // pthread_mutex_lock(&working_flag_mutex); pthread_mutex_lock(&search_flag_mutex); while (search_mutex_counter != 0){ pthread_mutex_unlock(&search_flag_mutex); usleep(1); pthread_mutex_lock(&search_flag_mutex); } search_mutex_counter = -1; //标记-1，执行替换 pthread_mutex_unlock(&search_flag_mutex); if (father_ptr->left_son_ptr == *Rebuild_Ptr) { //替换tree father_ptr->left_son_ptr = new_root_node; } else if (father_ptr->right_son_ptr == *Rebuild_Ptr){ father_ptr->right_son_ptr = new_root_node; } else { throw "Error: Father ptr incompatible with current node\n"; } if (new_root_node != nullptr) new_root_node->father_ptr = father_ptr; (*Rebuild_Ptr) = new_root_node; int valid_old = old_root_node->TreeSize-old_root_node->invalid_point_num; int valid_new = new_root_node->TreeSize-new_root_node->invalid_point_num; if (father_ptr == STATIC_ROOT_NODE) Root_Node = STATIC_ROOT_NODE->left_son_ptr; KD_TREE_NODE * update_root = *Rebuild_Ptr; while (update_root != nullptr && update_root != Root_Node){ update_root = update_root->father_ptr; if (update_root->working_flag) break; if (update_root == update_root->father_ptr->left_son_ptr && update_root->father_ptr->need_push_down_to_left) break; if (update_root == update_root->father_ptr->right_son_ptr && update_root->father_ptr->need_push_down_to_right) break; Update(update_root); } pthread_mutex_lock(&search_flag_mutex); search_mutex_counter = 0; pthread_mutex_unlock(&search_flag_mutex); Rebuild_Ptr = nullptr; pthread_mutex_unlock(&working_flag_mutex); rebuild_flag = false; /* Delete discarded tree nodes */ delete_tree_nodes(&old_root_node); // todo delete pointer to corresponding voxel, !!caution: before now node setting } else { pthread_mutex_unlock(&working_flag_mutex); } pthread_mutex_unlock(&rebuild_ptr_mutex_lock); pthread_mutex_lock(&termination_flag_mutex_lock); terminated = termination_flag; pthread_mutex_unlock(&termination_flag_mutex_lock); usleep(100); } printf("Rebuild thread terminated normally\n"); } template void KD_TREE::run_operation(KD_TREE_NODE ** root, Operation_Logger_Type operation){ switch (operation.op) { case ADD_POINT: Add_by_point(root, operation.point, false, (*root)->division_axis); break; case ADD_BOX: Add_by_range(root, operation.boxpoint, false); break; case DELETE_POINT: Delete_by_point(root, operation.point, false); break; case DELETE_BOX: Delete_by_range(root, operation.boxpoint, false, false); break; case DOWNSAMPLE_DELETE: Delete_by_range(root, operation.boxpoint, false, true); break; case PUSH_DOWN: (*root)->tree_downsample_deleted |= operation.tree_downsample_deleted; (*root)->point_downsample_deleted |= operation.tree_downsample_deleted; (*root)->tree_deleted = operation.tree_deleted || (*root)->tree_downsample_deleted; (*root)->point_deleted = (*root)->tree_deleted || (*root)->point_downsample_deleted; if (operation.tree_downsample_deleted) (*root)->down_del_num = (*root)->TreeSize; if (operation.tree_deleted) (*root)->invalid_point_num = (*root)->TreeSize; else (*root)->invalid_point_num = (*root)->down_del_num; (*root)->need_push_down_to_left = true; (*root)->need_push_down_to_right = true; break; default: break; } } template void KD_TREE::insideVoxelDownsample(PointVector& point_cloud) { // voxel2node.clear(); // node2voxel.clear(); unordered_map voxel2point; for (PointType& p : point_cloud) { // const PointType& p = root->point; float loc_xyz[3]; point2Voxel(p, loc_xyz); VOXEL_LOC voxel = VOXEL_LOC((int64_t)loc_xyz[0], (int64_t)loc_xyz[1], (int64_t)loc_xyz[2]); // VOXEL_LOC voxel; // point2Voxel(p, voxel); // auto iter = voxel2node.find(voxel); if (voxel2point.find(voxel) == voxel2point.end()) { //already have the voexl voxel2point[voxel] = p; // node2voxel[root] = voxel; // voxel_map[voxel]->temp_points_.push_back(p_v); // voxel_map[voxel]->new_points_num_++; } else { const PointType& p_old = voxel2point[voxel]; PointType mid_point; mid_point.x = loc_xyz[0]; mid_point.y = loc_xyz[1]; mid_point.z = loc_xyz[2]; float dist_old = calc_dist(p_old, mid_point); float dist_new = calc_dist(p, mid_point); if (dist_new < dist_old) { voxel2point[voxel] = p; } } } int i = 0; for (const auto& n : voxel2point) point_cloud[i++] = n.second; point_cloud.resize(i); } template void KD_TREE::Build(PointVector point_cloud){ if (Root_Node != nullptr){ delete_tree_nodes(&Root_Node); } if (point_cloud.size() == 0) return; //todo voxel grid downsample first // insideVoxelDownsample(point_cloud); STATIC_ROOT_NODE = new KD_TREE_NODE; InitTreeNode(STATIC_ROOT_NODE); BuildTree(&STATIC_ROOT_NODE->left_son_ptr, 0, point_cloud.size()-1, point_cloud); Update(STATIC_ROOT_NODE); STATIC_ROOT_NODE->TreeSize = 0; Root_Node = STATIC_ROOT_NODE->left_son_ptr; // todo build connection between voxels and nodes buildConnection(Root_Node); } template void KD_TREE::Nearest_Search(PointType point, int k_nearest, PointVector& Nearest_Points, vector & Point_Distance, double max_dist){ MANUAL_HEAP q(2*k_nearest); q.clear(); vector ().swap(Point_Distance); if (Rebuild_Ptr == nullptr || *Rebuild_Ptr != Root_Node){ Search(Root_Node, k_nearest, point, q, max_dist); } else { pthread_mutex_lock(&search_flag_mutex); while (search_mutex_counter == -1) { pthread_mutex_unlock(&search_flag_mutex); usleep(1); pthread_mutex_lock(&search_flag_mutex); } search_mutex_counter += 1; pthread_mutex_unlock(&search_flag_mutex); Search(Root_Node, k_nearest, point, q, max_dist); pthread_mutex_lock(&search_flag_mutex); search_mutex_counter -= 1; pthread_mutex_unlock(&search_flag_mutex); } int k_found = min(k_nearest,int(q.size())); PointVector ().swap(Nearest_Points); vector ().swap(Point_Distance); for (int i=0;i < k_found;i++){ Nearest_Points.insert(Nearest_Points.begin(), q.top().point); Point_Distance.insert(Point_Distance.begin(), q.top().dist); q.pop(); } return; } int six_neighbor[6] = {1, -1, 1, -1, 1, -1}; // dx, dy, dz template void KD_TREE::addNearVoxelsLoc(const PointType& point, const float* voxel_center, std::vector& voxels) { // int dimension = 0; // vector six_neighbor({1, -1, 1, -1, 1, -1}); //2-4-8 neighbors for (int dimension = 0; dimension < 3; ++dimension) { int d_dimension = 0; const float & coor = point.getVector3fMap()[dimension]; if (coor > (voxel_center[dimension] + quater_voxel_size)) { d_dimension = 1; six_neighbor[dimension * 2] = 0; } else if (coor < (voxel_center[dimension] - quater_voxel_size)) { d_dimension = -1; six_neighbor[dimension * 2 + 1] = 0; } if (d_dimension == 0) continue; // 2-4-8 neighbors first int size_old = voxels.size(); voxels.resize(size_old * 2); for (int i = 0; i < size_old / 3; ++i) { voxels[i * 3 + size_old] = voxels[i * 3]; voxels[i * 3 + 1 + size_old] = voxels[i * 3 + 1]; voxels[i * 3 + 2 + size_old] = voxels[i * 3 + 2]; voxels[i * 3 + size_old + dimension] += d_dimension; } } // 6 nearest neighbors int size_old = voxels.size(); for (int i = 0; i < 6; ++i) { if (six_neighbor[i] == 0) continue; int dimension = i / 2; voxels.emplace_back(voxels[0]); voxels.emplace_back(voxels[1]); voxels.emplace_back(voxels[2]); voxels[size_old + dimension] += six_neighbor[i]; size_old += 3; } } template void KD_TREE::updateVoxelInfo(const PointType& point, const Eigen::Matrix3f & cov) { VOXEL_LOC voxel; // float cube_center[3]; point2Voxel(point, voxel); auto iter = voxel2info.find(voxel); VoxelInfoPtr voxel_ptr; if (iter == voxel2info.end() || !iter->second) //for voxel that not initialize yet { voxel_ptr = new VoxelInfo(point); voxel2info[voxel] = voxel_ptr; return; } else voxel_ptr = iter->second; if (voxel_ptr->num_points > 0 && voxel_ptr->num_points < max_voxel_points_size) { // printf("num_points %d, surfel_points_min %d, max_voxel_points_size: %d.\n", voxel_ptr->num_points, // surfel_points_min, max_voxel_points_size); voxel_ptr->updateWithSingle(point, cov); } } template void KD_TREE::updateVoxelInfo(const PointType& point) { VOXEL_LOC voxel; // float cube_center[3]; point2Voxel(point, voxel); auto iter = voxel2info.find(voxel); VoxelInfoPtr voxel_ptr; if (iter == voxel2info.end() || !iter->second) //for voxel that not initialize yet { voxel_ptr = new VoxelInfo(point); voxel2info[voxel] = voxel_ptr; return; } else voxel_ptr = iter->second; if (voxel_ptr->num_points > 0 && voxel_ptr->num_points < max_voxel_points_size) voxel_ptr->updateWithSingle(point); } template void KD_TREE::Voxel_Search_test(const PointType& point, int k_nearest, PointVector &Nearest_Points, vector & Point_Distance, vector& voxels) { PointVector().swap(Nearest_Points); vector().swap(voxels); vector ().swap(Point_Distance); //divide into voxel float loc_xyz[3], center_xyz[3]; point2Voxel(point, loc_xyz, center_xyz); // printf("\npoint: %f, %f, %f loc_xyz: %f, %f, %f center_xyz: %f, %f, %f.\n", point.x, point.y, point.z, // loc_xyz[0], loc_xyz[1], loc_xyz[2], // center_xyz[0], center_xyz[1], center_xyz[2]); VOXEL_LOC position((int64_t)loc_xyz[0], (int64_t)loc_xyz[1], (int64_t)loc_xyz[2]); vector near_voxel_loc; near_voxel_loc.reserve(30); // record first voxel index; near_voxel_loc.emplace_back(loc_xyz[0]); near_voxel_loc.emplace_back(loc_xyz[1]); near_voxel_loc.emplace_back(loc_xyz[2]); addNearVoxelsLoc(point, center_xyz, near_voxel_loc); MANUAL_VOXEL_HEAP q(k_nearest); q.clear(); for (int i = 0; i < near_voxel_loc.size() / 3; ++i) { // printf("near_loc_xyz: %f, %f, %f\n", near_voxel_loc[i * 3], near_voxel_loc[i * 3 + 1], near_voxel_loc[i * 3 + 2]); VOXEL_LOC near_position((int64_t)near_voxel_loc[i * 3], (int64_t)near_voxel_loc[i * 3 + 1], near_voxel_loc[i * 3 + 2]); // const auto & near_iter = voxel2node.find(near_position); const auto & near_iter = voxel2info.find(near_position); if (near_iter != voxel2info.end()) { VoxelInfo* voxel_info = near_iter->second; // const PointType& p = near_iter->second->point; // float dist = calc_dist(p, point); if (voxel_info) { PointType p; // while (voxel_info->flag_updating) /// !!!caution // usleep(2); lock_guard lock(voxel_info->updating_mutex_lock); // pthread_mutex_lock(&voxel_info->updating_mutex_lock); p.x = voxel_info->mean_x; p.y = voxel_info->mean_y; p.z = voxel_info->mean_z; // pthread_mutex_unlock(&voxel_info->updating_mutex_lock); float dist = calc_dist(point, p); // if (dist <= max_dist_sqr && (q.size() < k_nearest || dist < q.top().dist)){ if (q.size() < k_nearest || dist < q.top().dist) { if (q.size() >= k_nearest) q.pop(); PointType_Voxel_CMP current_point{p, voxel_info, dist}; q.push(current_point); } } } } int k_found = min(k_nearest,int(q.size())); for (int i=0;i < k_found;i++){ Nearest_Points.insert(Nearest_Points.begin(), q.top().point); Point_Distance.insert(Point_Distance.begin(), q.top().dist); voxels.insert(voxels.begin(), q.top().info); // q.top().info->getSimpInfo(s_info); q.pop(); } } template void KD_TREE::Voxel_Search(const PointType& point, int k_nearest, PointVector &Nearest_Points, vector& nodes) { //divide into voxel float loc_xyz[3], center_xyz[3]; point2Voxel(point, loc_xyz, center_xyz); VOXEL_LOC position((int64_t)loc_xyz[0], (int64_t)loc_xyz[1], (int64_t)loc_xyz[2]); const auto & iter = voxel2node.find(position); if (iter == voxel2node.end()) return; KD_TREE_NODE* node_center = iter->second; nodes.emplace_back(node_center); Nearest_Points.push_back(node_center->point); VOXEL_LOC near_position = position; } template void KD_TREE::Nearest_Search(PointType point, int k_nearest, PointVector &Nearest_Points, vector & Point_Distance, vector& voxels, double max_dist) { MANUAL_HEAP q(2*k_nearest); q.clear(); vector ().swap(Point_Distance); if (Rebuild_Ptr == nullptr || *Rebuild_Ptr != Root_Node){ // 没有rebuild,直接search Search(Root_Node, k_nearest, point, q, max_dist); } else { // 如果有rebuild pthread_mutex_lock(&search_flag_mutex); //search_flag_mutex while (search_mutex_counter == -1) { pthread_mutex_unlock(&search_flag_mutex);//search_flag_mutex usleep(1); pthread_mutex_lock(&search_flag_mutex);//search_flag_mutex } search_mutex_counter += 1; // +1? pthread_mutex_unlock(&search_flag_mutex); Search(Root_Node, k_nearest, point, q, max_dist); pthread_mutex_lock(&search_flag_mutex); search_mutex_counter -= 1; pthread_mutex_unlock(&search_flag_mutex); } int k_found = min(k_nearest,int(q.size())); PointVector ().swap(Nearest_Points); vector ().swap(Point_Distance); vector ().swap(voxels); for (int i=0;i < k_found;i++){ Nearest_Points.insert(Nearest_Points.begin(), q.top().point); Point_Distance.insert(Point_Distance.begin(), q.top().dist); VOXEL_LOC voxel; point2Voxel(q.top().point, voxel); auto iter = voxel2info.find(voxel); VoxelInfoPtr voxel_ptr; if (iter == voxel2info.end() || !iter->second) //for voxel that not initialize yet { voxel_ptr = new VoxelInfo(point); voxel2info[voxel] = voxel_ptr; return; } else voxel_ptr = iter->second; voxels.insert(voxels.begin(), voxel_ptr); q.pop(); } return; } template void KD_TREE::Box_Search(const BoxPointType &Box_of_Point, PointVector &Storage) { Storage.clear(); Search_by_range(Root_Node, Box_of_Point, Storage); } template void KD_TREE::Radius_Search(PointType point, const float radius, PointVector &Storage) { Storage.clear(); Search_by_radius(Root_Node, point, radius, Storage); } template int KD_TREE::Add_Points(PointVector & PointToAdd, bool downsample_on){ int NewPointSize = PointToAdd.size(); int tree_size = size(); BoxPointType Box_of_Point; PointType downsample_result, mid_point; bool downsample_switch = downsample_on && DOWNSAMPLE_SWITCH; float min_dist, tmp_dist; int tmp_counter = 0; for (int i=0; i 1 || same_point(PointToAdd[i], downsample_result)){ if (Downsample_Storage.size() > 0) Delete_by_range(&Root_Node, Box_of_Point, true, true); Add_by_point(&Root_Node, downsample_result, true, Root_Node->division_axis); tmp_counter ++; } } else { if (Downsample_Storage.size() > 1 || same_point(PointToAdd[i], downsample_result)){ Operation_Logger_Type operation_delete, operation; operation_delete.boxpoint = Box_of_Point; operation_delete.op = DOWNSAMPLE_DELETE; operation.point = downsample_result; operation.op = ADD_POINT; pthread_mutex_lock(&working_flag_mutex); if (Downsample_Storage.size() > 0) Delete_by_range(&Root_Node, Box_of_Point, false , true); Add_by_point(&Root_Node, downsample_result, false, Root_Node->division_axis); tmp_counter ++; if (rebuild_flag){ pthread_mutex_lock(&rebuild_logger_mutex_lock); if (Downsample_Storage.size() > 0) Rebuild_Logger.push(operation_delete); Rebuild_Logger.push(operation); pthread_mutex_unlock(&rebuild_logger_mutex_lock); } pthread_mutex_unlock(&working_flag_mutex); }; } } else { if (Rebuild_Ptr == nullptr || *Rebuild_Ptr != Root_Node){ Add_by_point(&Root_Node, PointToAdd[i], true, Root_Node->division_axis); } else { Operation_Logger_Type operation; operation.point = PointToAdd[i]; operation.op = ADD_POINT; pthread_mutex_lock(&working_flag_mutex); Add_by_point(&Root_Node, PointToAdd[i], false, Root_Node->division_axis); if (rebuild_flag){ pthread_mutex_lock(&rebuild_logger_mutex_lock); Rebuild_Logger.push(operation); pthread_mutex_unlock(&rebuild_logger_mutex_lock); } pthread_mutex_unlock(&working_flag_mutex); } } } return tmp_counter; } template void KD_TREE::Add_Point_Boxes(vector & BoxPoints){ for (int i=0;i < BoxPoints.size();i++){ if (Rebuild_Ptr == nullptr || *Rebuild_Ptr != Root_Node){ Add_by_range(&Root_Node ,BoxPoints[i], true); } else { Operation_Logger_Type operation; operation.boxpoint = BoxPoints[i]; operation.op = ADD_BOX; pthread_mutex_lock(&working_flag_mutex); Add_by_range(&Root_Node ,BoxPoints[i], false); if (rebuild_flag){ pthread_mutex_lock(&rebuild_logger_mutex_lock); Rebuild_Logger.push(operation); pthread_mutex_unlock(&rebuild_logger_mutex_lock); } pthread_mutex_unlock(&working_flag_mutex); } } return; } template void KD_TREE::Delete_Points(PointVector & PointToDel){ for (int i=0;i int KD_TREE::Delete_Point_Boxes(vector & BoxPoints){ int tmp_counter = 0; for (int i=0;i < BoxPoints.size();i++){ if (Rebuild_Ptr == nullptr || *Rebuild_Ptr != Root_Node){ tmp_counter += Delete_by_range(&Root_Node ,BoxPoints[i], true, false); } else { Operation_Logger_Type operation; operation.boxpoint = BoxPoints[i]; operation.op = DELETE_BOX; pthread_mutex_lock(&working_flag_mutex); tmp_counter += Delete_by_range(&Root_Node ,BoxPoints[i], false, false); if (rebuild_flag){ pthread_mutex_lock(&rebuild_logger_mutex_lock); Rebuild_Logger.push(operation); pthread_mutex_unlock(&rebuild_logger_mutex_lock); } pthread_mutex_unlock(&working_flag_mutex); } } return tmp_counter; } template void KD_TREE::acquire_removed_points(PointVector & removed_points){ pthread_mutex_lock(&points_deleted_rebuild_mutex_lock); for (int i = 0; i < Points_deleted.size();i++){ removed_points.push_back(Points_deleted[i]); } for (int i = 0; i < Multithread_Points_deleted.size();i++){ removed_points.push_back(Multithread_Points_deleted[i]); } Points_deleted.clear(); Multithread_Points_deleted.clear(); pthread_mutex_unlock(&points_deleted_rebuild_mutex_lock); return; } template void KD_TREE::findVoxel(const PointType& p) { // //divide into voxel // float loc_xyz[3]; // loc_xyz[0] = p.x / downsample_size; // loc_xyz[1] = p.y / downsample_size; // loc_xyz[2] = p.z / downsample_size; // for (int j = 0; j < 3; j++) // if (loc_xyz[j] < 0) // loc_xyz[j] -= 1.0; // // VOXEL_LOC position((int64_t)loc_xyz[0], (int64_t)loc_xyz[1], (int64_t)loc_xyz[2]); // auto iter = voxel_map.find(position); // // pointWithCov p_v; // p_v.point_world << p.x, p.y, p.z; // p.getPointCovMatrix(p_v.cov); // if (iter != voxel_map.end()) { // //already have the voexl // voxel_map[position]->temp_points_.push_back(p_v); // voxel_map[position]->new_points_num_++; // } else { // OctoTree *octo_tree = // new OctoTree(max_layer, 0, layer_point_size, max_points_size, // max_cov_points_size, planer_threshold); // voxel_map[position] = octo_tree; // voxel_map[position]->quater_length_ = voxel_size / 4; // voxel_map[position]->voxel_center_[0] = (0.5 + position.x) * voxel_size; // voxel_map[position]->voxel_center_[1] = (0.5 + position.y) * voxel_size; // voxel_map[position]->voxel_center_[2] = (0.5 + position.z) * voxel_size; // voxel_map[position]->temp_points_.push_back(p_v); // voxel_map[position]->new_points_num_++; // voxel_map[position]->layer_point_size_ = layer_point_size; // } } template void KD_TREE::BuildTree(KD_TREE_NODE ** root, int l, int r, PointVector & Storage){ if (l>r) return; *root = new KD_TREE_NODE; InitTreeNode(*root); int mid = (l+r)>>1; int div_axis = 0; int i; // Find the best division Axis float min_value[3] = {INFINITY, INFINITY, INFINITY}; float max_value[3] = {-INFINITY, -INFINITY, -INFINITY}; float dim_range[3] = {0,0,0}; for (i=l;i<=r;i++){ min_value[0] = min(min_value[0], Storage[i].x); min_value[1] = min(min_value[1], Storage[i].y); min_value[2] = min(min_value[2], Storage[i].z); max_value[0] = max(max_value[0], Storage[i].x); max_value[1] = max(max_value[1], Storage[i].y); max_value[2] = max(max_value[2], Storage[i].z); } // Select the longest dimension as division axis for (i=0;i<3;i++) dim_range[i] = max_value[i] - min_value[i]; for (i=1;i<3;i++) if (dim_range[i] > dim_range[div_axis]) div_axis = i; // Divide by the division axis and recursively build. (*root)->division_axis = div_axis; switch (div_axis) { case 0: nth_element(begin(Storage)+l, begin(Storage)+mid, begin(Storage)+r+1, point_cmp_x); break; case 1: nth_element(begin(Storage)+l, begin(Storage)+mid, begin(Storage)+r+1, point_cmp_y); break; case 2: nth_element(begin(Storage)+l, begin(Storage)+mid, begin(Storage)+r+1, point_cmp_z); break; default: nth_element(begin(Storage)+l, begin(Storage)+mid, begin(Storage)+r+1, point_cmp_x); break; } (*root)->point = Storage[mid]; // connect to voxel single_connect(*root); KD_TREE_NODE * left_son = nullptr, * right_son = nullptr; BuildTree(&left_son, l, mid-1, Storage); BuildTree(&right_son, mid+1, r, Storage); (*root)->left_son_ptr = left_son; (*root)->right_son_ptr = right_son; Update((*root)); return; } template void KD_TREE::Rebuild(KD_TREE_NODE ** root){ KD_TREE_NODE * father_ptr; if ((*root)->TreeSize >= Multi_Thread_Rebuild_Point_Num) { if (!pthread_mutex_trylock(&rebuild_ptr_mutex_lock)){ if (Rebuild_Ptr == nullptr || ((*root)->TreeSize > (*Rebuild_Ptr)->TreeSize)) { Rebuild_Ptr = root; } pthread_mutex_unlock(&rebuild_ptr_mutex_lock); } } else { father_ptr = (*root)->father_ptr; int size_rec = (*root)->TreeSize; PCL_Storage.clear(); flatten(*root, PCL_Storage, DELETE_POINTS_REC); delete_tree_nodes(root); // todo delete pointer to corresponding voxel BuildTree(root, 0, PCL_Storage.size()-1, PCL_Storage); if (*root != nullptr) (*root)->father_ptr = father_ptr; if (*root == Root_Node) STATIC_ROOT_NODE->left_son_ptr = *root; } return; } template int KD_TREE::Delete_by_range(KD_TREE_NODE ** root, BoxPointType boxpoint, bool allow_rebuild, bool is_downsample){ if ((*root) == nullptr || (*root)->tree_deleted) return 0; (*root)->working_flag = true; Push_Down(*root); int tmp_counter = 0; if (boxpoint.vertex_max[0] <= (*root)->node_range_x[0] || boxpoint.vertex_min[0] > (*root)->node_range_x[1]) return 0; if (boxpoint.vertex_max[1] <= (*root)->node_range_y[0] || boxpoint.vertex_min[1] > (*root)->node_range_y[1]) return 0; if (boxpoint.vertex_max[2] <= (*root)->node_range_z[0] || boxpoint.vertex_min[2] > (*root)->node_range_z[1]) return 0; if (boxpoint.vertex_min[0] <= (*root)->node_range_x[0] && boxpoint.vertex_max[0] > (*root)->node_range_x[1] && boxpoint.vertex_min[1] <= (*root)->node_range_y[0] && boxpoint.vertex_max[1] > (*root)->node_range_y[1] && boxpoint.vertex_min[2] <= (*root)->node_range_z[0] && boxpoint.vertex_max[2] > (*root)->node_range_z[1]){ (*root)->tree_deleted = true; (*root)->point_deleted = true; (*root)->need_push_down_to_left = true; (*root)->need_push_down_to_right = true; tmp_counter = (*root)->TreeSize - (*root)->invalid_point_num; (*root)->invalid_point_num = (*root)->TreeSize; if (is_downsample){ (*root)->tree_downsample_deleted = true; (*root)->point_downsample_deleted = true; (*root)->down_del_num = (*root)->TreeSize; } return tmp_counter; } if (!(*root)->point_deleted && boxpoint.vertex_min[0] <= (*root)->point.x && boxpoint.vertex_max[0] > (*root)->point.x && boxpoint.vertex_min[1] <= (*root)->point.y && boxpoint.vertex_max[1] > (*root)->point.y && boxpoint.vertex_min[2] <= (*root)->point.z && boxpoint.vertex_max[2] > (*root)->point.z){ (*root)->point_deleted = true; tmp_counter += 1; if (is_downsample) (*root)->point_downsample_deleted = true; } Operation_Logger_Type delete_box_log; struct timespec Timeout; if (is_downsample) delete_box_log.op = DOWNSAMPLE_DELETE; else delete_box_log.op = DELETE_BOX; delete_box_log.boxpoint = boxpoint; if ((Rebuild_Ptr == nullptr) || (*root)->left_son_ptr != *Rebuild_Ptr){ tmp_counter += Delete_by_range(&((*root)->left_son_ptr), boxpoint, allow_rebuild, is_downsample); } else { pthread_mutex_lock(&working_flag_mutex); tmp_counter += Delete_by_range(&((*root)->left_son_ptr), boxpoint, false, is_downsample); if (rebuild_flag){ pthread_mutex_lock(&rebuild_logger_mutex_lock); Rebuild_Logger.push(delete_box_log); pthread_mutex_unlock(&rebuild_logger_mutex_lock); } pthread_mutex_unlock(&working_flag_mutex); } if ((Rebuild_Ptr == nullptr) || (*root)->right_son_ptr != *Rebuild_Ptr){ tmp_counter += Delete_by_range(&((*root)->right_son_ptr), boxpoint, allow_rebuild, is_downsample); } else { pthread_mutex_lock(&working_flag_mutex); tmp_counter += Delete_by_range(&((*root)->right_son_ptr), boxpoint, false, is_downsample); if (rebuild_flag){ pthread_mutex_lock(&rebuild_logger_mutex_lock); Rebuild_Logger.push(delete_box_log); pthread_mutex_unlock(&rebuild_logger_mutex_lock); } pthread_mutex_unlock(&working_flag_mutex); } Update(*root); if (Rebuild_Ptr != nullptr && *Rebuild_Ptr == *root && (*root)->TreeSize < Multi_Thread_Rebuild_Point_Num) Rebuild_Ptr = nullptr; bool need_rebuild = allow_rebuild & Criterion_Check((*root)); if (need_rebuild) Rebuild(root); if ((*root) != nullptr) (*root)->working_flag = false; return tmp_counter; } template void KD_TREE::Delete_by_point(KD_TREE_NODE ** root, PointType point, bool allow_rebuild){ if ((*root) == nullptr || (*root)->tree_deleted) return; (*root)->working_flag = true; Push_Down(*root); if (same_point((*root)->point, point) && !(*root)->point_deleted) { (*root)->point_deleted = true; (*root)->invalid_point_num += 1; if ((*root)->invalid_point_num == (*root)->TreeSize) (*root)->tree_deleted = true; return; } Operation_Logger_Type delete_log; struct timespec Timeout; delete_log.op = DELETE_POINT; delete_log.point = point; if (((*root)->division_axis == 0 && point.x < (*root)->point.x) || ((*root)->division_axis == 1 && point.y < (*root)->point.y) || ((*root)->division_axis == 2 && point.z < (*root)->point.z)){ if ((Rebuild_Ptr == nullptr) || (*root)->left_son_ptr != *Rebuild_Ptr){ Delete_by_point(&(*root)->left_son_ptr, point, allow_rebuild); } else { pthread_mutex_lock(&working_flag_mutex); Delete_by_point(&(*root)->left_son_ptr, point,false); if (rebuild_flag){ pthread_mutex_lock(&rebuild_logger_mutex_lock); Rebuild_Logger.push(delete_log); pthread_mutex_unlock(&rebuild_logger_mutex_lock); } pthread_mutex_unlock(&working_flag_mutex); } } else { if ((Rebuild_Ptr == nullptr) || (*root)->right_son_ptr != *Rebuild_Ptr){ Delete_by_point(&(*root)->right_son_ptr, point, allow_rebuild); } else { pthread_mutex_lock(&working_flag_mutex); Delete_by_point(&(*root)->right_son_ptr, point, false); if (rebuild_flag){ pthread_mutex_lock(&rebuild_logger_mutex_lock); Rebuild_Logger.push(delete_log); pthread_mutex_unlock(&rebuild_logger_mutex_lock); } pthread_mutex_unlock(&working_flag_mutex); } } Update(*root); if (Rebuild_Ptr != nullptr && *Rebuild_Ptr == *root && (*root)->TreeSize < Multi_Thread_Rebuild_Point_Num) Rebuild_Ptr = nullptr; bool need_rebuild = allow_rebuild & Criterion_Check((*root)); if (need_rebuild) Rebuild(root); if ((*root) != nullptr) (*root)->working_flag = false; return; } template void KD_TREE::Add_by_range(KD_TREE_NODE ** root, BoxPointType boxpoint, bool allow_rebuild){ if ((*root) == nullptr) return; (*root)->working_flag = true; Push_Down(*root); if (boxpoint.vertex_max[0] <= (*root)->node_range_x[0] || boxpoint.vertex_min[0] > (*root)->node_range_x[1]) return; if (boxpoint.vertex_max[1] <= (*root)->node_range_y[0] || boxpoint.vertex_min[1] > (*root)->node_range_y[1]) return; if (boxpoint.vertex_max[2] <= (*root)->node_range_z[0] || boxpoint.vertex_min[2] > (*root)->node_range_z[1]) return; if (boxpoint.vertex_min[0] <= (*root)->node_range_x[0] && boxpoint.vertex_max[0] > (*root)->node_range_x[1] && boxpoint.vertex_min[1] <= (*root)->node_range_y[0] && boxpoint.vertex_max[1]> (*root)->node_range_y[1] && boxpoint.vertex_min[2] <= (*root)->node_range_z[0] && boxpoint.vertex_max[2] > (*root)->node_range_z[1]){ (*root)->tree_deleted = false || (*root)->tree_downsample_deleted; (*root)->point_deleted = false || (*root)->point_downsample_deleted; (*root)->need_push_down_to_left = true; (*root)->need_push_down_to_right = true; (*root)->invalid_point_num = (*root)->down_del_num; // todo set pointer to corresponding voxel return; } if (boxpoint.vertex_min[0] <= (*root)->point.x && boxpoint.vertex_max[0] > (*root)->point.x && boxpoint.vertex_min[1] <= (*root)->point.y && boxpoint.vertex_max[1] > (*root)->point.y && boxpoint.vertex_min[2] <= (*root)->point.z && boxpoint.vertex_max[2] > (*root)->point.z){ (*root)->point_deleted = (*root)->point_downsample_deleted; } Operation_Logger_Type add_box_log; struct timespec Timeout; add_box_log.op = ADD_BOX; add_box_log.boxpoint = boxpoint; if ((Rebuild_Ptr == nullptr) || (*root)->left_son_ptr != *Rebuild_Ptr){ Add_by_range(&((*root)->left_son_ptr), boxpoint, allow_rebuild); } else { pthread_mutex_lock(&working_flag_mutex); Add_by_range(&((*root)->left_son_ptr), boxpoint, false); if (rebuild_flag){ pthread_mutex_lock(&rebuild_logger_mutex_lock); Rebuild_Logger.push(add_box_log); pthread_mutex_unlock(&rebuild_logger_mutex_lock); } pthread_mutex_unlock(&working_flag_mutex); } if ((Rebuild_Ptr == nullptr) || (*root)->right_son_ptr != *Rebuild_Ptr){ Add_by_range(&((*root)->right_son_ptr), boxpoint, allow_rebuild); } else { pthread_mutex_lock(&working_flag_mutex); Add_by_range(&((*root)->right_son_ptr), boxpoint, false); if (rebuild_flag){ pthread_mutex_lock(&rebuild_logger_mutex_lock); Rebuild_Logger.push(add_box_log); pthread_mutex_unlock(&rebuild_logger_mutex_lock); } pthread_mutex_unlock(&working_flag_mutex); } Update(*root); if (Rebuild_Ptr != nullptr && *Rebuild_Ptr == *root && (*root)->TreeSize < Multi_Thread_Rebuild_Point_Num) Rebuild_Ptr = nullptr; bool need_rebuild = allow_rebuild & Criterion_Check((*root)); if (need_rebuild) Rebuild(root); if ((*root) != nullptr) (*root)->working_flag = false; return; } template void KD_TREE::Add_by_point(KD_TREE_NODE ** root, PointType point, bool allow_rebuild, int father_axis){ if (*root == nullptr){ *root = new KD_TREE_NODE; InitTreeNode(*root); (*root)->point = point; single_connect(*root); (*root)->division_axis = (father_axis + 1) % 3; Update(*root); return; } (*root)->working_flag = true; Operation_Logger_Type add_log; struct timespec Timeout; add_log.op = ADD_POINT; add_log.point = point; Push_Down(*root); // left or right son if (((*root)->division_axis == 0 && point.x < (*root)->point.x) || ((*root)->division_axis == 1 && point.y < (*root)->point.y) || ((*root)->division_axis == 2 && point.z < (*root)->point.z)){ if ((Rebuild_Ptr == nullptr) || (*root)->left_son_ptr != *Rebuild_Ptr){ Add_by_point(&(*root)->left_son_ptr, point, allow_rebuild, (*root)->division_axis); } else { pthread_mutex_lock(&working_flag_mutex); Add_by_point(&(*root)->left_son_ptr, point, false,(*root)->division_axis); if (rebuild_flag){ pthread_mutex_lock(&rebuild_logger_mutex_lock); Rebuild_Logger.push(add_log); pthread_mutex_unlock(&rebuild_logger_mutex_lock); } pthread_mutex_unlock(&working_flag_mutex); } } else { if ((Rebuild_Ptr == nullptr) || (*root)->right_son_ptr != *Rebuild_Ptr){ Add_by_point(&(*root)->right_son_ptr, point, allow_rebuild,(*root)->division_axis); } else { pthread_mutex_lock(&working_flag_mutex); Add_by_point(&(*root)->right_son_ptr, point, false,(*root)->division_axis); if (rebuild_flag){ pthread_mutex_lock(&rebuild_logger_mutex_lock); Rebuild_Logger.push(add_log); pthread_mutex_unlock(&rebuild_logger_mutex_lock); } pthread_mutex_unlock(&working_flag_mutex); } } Update(*root); if (Rebuild_Ptr != nullptr && *Rebuild_Ptr == *root && (*root)->TreeSize < Multi_Thread_Rebuild_Point_Num) Rebuild_Ptr = nullptr; bool need_rebuild = allow_rebuild & Criterion_Check((*root)); if (need_rebuild) Rebuild(root); if ((*root) != nullptr) (*root)->working_flag = false; return; } template void KD_TREE::Search(KD_TREE_NODE * root, int k_nearest, PointType point, MANUAL_HEAP &q, double max_dist){ if (root == nullptr || root->tree_deleted) return; double cur_dist = calc_box_dist(root, point); double max_dist_sqr = max_dist * max_dist; if (cur_dist > max_dist_sqr) return; int retval; if (root->need_push_down_to_left || root->need_push_down_to_right) { retval = pthread_mutex_trylock(&(root->push_down_mutex_lock)); if (retval == 0){ Push_Down(root); pthread_mutex_unlock(&(root->push_down_mutex_lock)); } else { pthread_mutex_lock(&(root->push_down_mutex_lock)); pthread_mutex_unlock(&(root->push_down_mutex_lock)); } } if (!root->point_deleted){ float dist = calc_dist(point, root->point); if (dist <= max_dist_sqr && (q.size() < k_nearest || dist < q.top().dist)){ if (q.size() >= k_nearest) q.pop(); PointType_CMP current_point{root->point, root, dist}; q.push(current_point); } } int cur_search_counter; float dist_left_node = calc_box_dist(root->left_son_ptr, point); float dist_right_node = calc_box_dist(root->right_son_ptr, point); if (q.size()< k_nearest || dist_left_node < q.top().dist && dist_right_node < q.top().dist){ if (dist_left_node <= dist_right_node) { if (Rebuild_Ptr == nullptr || *Rebuild_Ptr != root->left_son_ptr){ Search(root->left_son_ptr, k_nearest, point, q, max_dist); } else { pthread_mutex_lock(&search_flag_mutex); while (search_mutex_counter == -1) { pthread_mutex_unlock(&search_flag_mutex); usleep(1); pthread_mutex_lock(&search_flag_mutex); } search_mutex_counter += 1; pthread_mutex_unlock(&search_flag_mutex); Search(root->left_son_ptr, k_nearest, point, q, max_dist); pthread_mutex_lock(&search_flag_mutex); search_mutex_counter -= 1; pthread_mutex_unlock(&search_flag_mutex); } if (q.size() < k_nearest || dist_right_node < q.top().dist) { if (Rebuild_Ptr == nullptr || *Rebuild_Ptr != root->right_son_ptr){ Search(root->right_son_ptr, k_nearest, point, q, max_dist); } else { pthread_mutex_lock(&search_flag_mutex); while (search_mutex_counter == -1) { pthread_mutex_unlock(&search_flag_mutex); usleep(1); pthread_mutex_lock(&search_flag_mutex); } search_mutex_counter += 1; pthread_mutex_unlock(&search_flag_mutex); Search(root->right_son_ptr, k_nearest, point, q, max_dist); pthread_mutex_lock(&search_flag_mutex); search_mutex_counter -= 1; pthread_mutex_unlock(&search_flag_mutex); } } } else { if (Rebuild_Ptr == nullptr || *Rebuild_Ptr != root->right_son_ptr){ Search(root->right_son_ptr, k_nearest, point, q, max_dist); } else { pthread_mutex_lock(&search_flag_mutex); while (search_mutex_counter == -1) { pthread_mutex_unlock(&search_flag_mutex); usleep(1); pthread_mutex_lock(&search_flag_mutex); } search_mutex_counter += 1; pthread_mutex_unlock(&search_flag_mutex); Search(root->right_son_ptr, k_nearest, point, q, max_dist); pthread_mutex_lock(&search_flag_mutex); search_mutex_counter -= 1; pthread_mutex_unlock(&search_flag_mutex); } if (q.size() < k_nearest || dist_left_node < q.top().dist) { if (Rebuild_Ptr == nullptr || *Rebuild_Ptr != root->left_son_ptr){ Search(root->left_son_ptr, k_nearest, point, q, max_dist); } else { pthread_mutex_lock(&search_flag_mutex); while (search_mutex_counter == -1) { pthread_mutex_unlock(&search_flag_mutex); usleep(1); pthread_mutex_lock(&search_flag_mutex); } search_mutex_counter += 1; pthread_mutex_unlock(&search_flag_mutex); Search(root->left_son_ptr, k_nearest, point, q, max_dist); pthread_mutex_lock(&search_flag_mutex); search_mutex_counter -= 1; pthread_mutex_unlock(&search_flag_mutex); } } } } else { if (dist_left_node < q.top().dist) { if (Rebuild_Ptr == nullptr || *Rebuild_Ptr != root->left_son_ptr){ Search(root->left_son_ptr, k_nearest, point, q, max_dist); } else { pthread_mutex_lock(&search_flag_mutex); while (search_mutex_counter == -1) { pthread_mutex_unlock(&search_flag_mutex); usleep(1); pthread_mutex_lock(&search_flag_mutex); } search_mutex_counter += 1; pthread_mutex_unlock(&search_flag_mutex); Search(root->left_son_ptr, k_nearest, point, q, max_dist); pthread_mutex_lock(&search_flag_mutex); search_mutex_counter -= 1; pthread_mutex_unlock(&search_flag_mutex); } } if (dist_right_node < q.top().dist) { if (Rebuild_Ptr == nullptr || *Rebuild_Ptr != root->right_son_ptr){ Search(root->right_son_ptr, k_nearest, point, q, max_dist); } else { pthread_mutex_lock(&search_flag_mutex); while (search_mutex_counter == -1) { pthread_mutex_unlock(&search_flag_mutex); usleep(1); pthread_mutex_lock(&search_flag_mutex); } search_mutex_counter += 1; pthread_mutex_unlock(&search_flag_mutex); Search(root->right_son_ptr, k_nearest, point, q, max_dist); pthread_mutex_lock(&search_flag_mutex); search_mutex_counter -= 1; pthread_mutex_unlock(&search_flag_mutex); } } } return; } template void KD_TREE::Search_by_range(KD_TREE_NODE *root, BoxPointType boxpoint, PointVector & Storage){ if (root == nullptr) return; Push_Down(root); if (boxpoint.vertex_max[0] <= root->node_range_x[0] || boxpoint.vertex_min[0] > root->node_range_x[1]) return; if (boxpoint.vertex_max[1] <= root->node_range_y[0] || boxpoint.vertex_min[1] > root->node_range_y[1]) return; if (boxpoint.vertex_max[2] <= root->node_range_z[0] || boxpoint.vertex_min[2] > root->node_range_z[1]) return; if (boxpoint.vertex_min[0] <= root->node_range_x[0] && boxpoint.vertex_max[0] > root->node_range_x[1] && boxpoint.vertex_min[1] <= root->node_range_y[0] && boxpoint.vertex_max[1] > root->node_range_y[1] && boxpoint.vertex_min[2] <= root->node_range_z[0] && boxpoint.vertex_max[2] > root->node_range_z[1]){ flatten(root, Storage, NOT_RECORD); return; } if (boxpoint.vertex_min[0] <= root->point.x && boxpoint.vertex_max[0] > root->point.x && boxpoint.vertex_min[1] <= root->point.y && boxpoint.vertex_max[1] > root->point.y && boxpoint.vertex_min[2] <= root->point.z && boxpoint.vertex_max[2] > root->point.z){ if (!root->point_deleted) Storage.push_back(root->point); } if ((Rebuild_Ptr == nullptr) || root->left_son_ptr != *Rebuild_Ptr){ Search_by_range(root->left_son_ptr, boxpoint, Storage); } else { pthread_mutex_lock(&search_flag_mutex); Search_by_range(root->left_son_ptr, boxpoint, Storage); pthread_mutex_unlock(&search_flag_mutex); } if ((Rebuild_Ptr == nullptr) || root->right_son_ptr != *Rebuild_Ptr){ Search_by_range(root->right_son_ptr, boxpoint, Storage); } else { pthread_mutex_lock(&search_flag_mutex); Search_by_range(root->right_son_ptr, boxpoint, Storage); pthread_mutex_unlock(&search_flag_mutex); } return; } template void KD_TREE::Search_by_radius(KD_TREE_NODE *root, PointType point, float radius, PointVector &Storage) { if (root == nullptr) return; Push_Down(root); PointType range_center; range_center.x = (root->node_range_x[0] + root->node_range_x[1]) * 0.5; range_center.y = (root->node_range_y[0] + root->node_range_y[1]) * 0.5; range_center.z = (root->node_range_z[0] + root->node_range_z[1]) * 0.5; float dist = sqrt(calc_dist(range_center, point)); if (dist > radius + sqrt(root->radius_sq)) return; if (dist <= radius - sqrt(root->radius_sq)) { flatten(root, Storage, NOT_RECORD); return; } if (!root->point_deleted && calc_dist(root->point, point) <= radius * radius){ Storage.push_back(root->point); } if ((Rebuild_Ptr == nullptr) || root->left_son_ptr != *Rebuild_Ptr) { Search_by_radius(root->left_son_ptr, point, radius, Storage); } else { pthread_mutex_lock(&search_flag_mutex); Search_by_radius(root->left_son_ptr, point, radius, Storage); pthread_mutex_unlock(&search_flag_mutex); } if ((Rebuild_Ptr == nullptr) || root->right_son_ptr != *Rebuild_Ptr) { Search_by_radius(root->right_son_ptr, point, radius, Storage); } else { pthread_mutex_lock(&search_flag_mutex); Search_by_radius(root->right_son_ptr, point, radius, Storage); pthread_mutex_unlock(&search_flag_mutex); } return; } template bool KD_TREE::Criterion_Check(KD_TREE_NODE * root){ if (root->TreeSize <= Minimal_Unbalanced_Tree_Size){ return false; } float balance_evaluation = 0.0f; float delete_evaluation = 0.0f; KD_TREE_NODE * son_ptr = root->left_son_ptr; if (son_ptr == nullptr) son_ptr = root->right_son_ptr; delete_evaluation = float(root->invalid_point_num)/ root->TreeSize; balance_evaluation = float(son_ptr->TreeSize) / (root->TreeSize-1); if (delete_evaluation > delete_criterion_param){ return true; } if (balance_evaluation > balance_criterion_param || balance_evaluation < 1-balance_criterion_param){ return true; } return false; } template void KD_TREE::Push_Down(KD_TREE_NODE *root){ if (root == nullptr) return; Operation_Logger_Type operation; operation.op = PUSH_DOWN; operation.tree_deleted = root->tree_deleted; operation.tree_downsample_deleted = root->tree_downsample_deleted; if (root->need_push_down_to_left && root->left_son_ptr != nullptr){ if (Rebuild_Ptr == nullptr || *Rebuild_Ptr != root->left_son_ptr){ root->left_son_ptr->tree_downsample_deleted |= root->tree_downsample_deleted; root->left_son_ptr->point_downsample_deleted |= root->tree_downsample_deleted; root->left_son_ptr->tree_deleted = root->tree_deleted || root->left_son_ptr->tree_downsample_deleted; root->left_son_ptr->point_deleted = root->left_son_ptr->tree_deleted || root->left_son_ptr->point_downsample_deleted; if (root->tree_downsample_deleted) root->left_son_ptr->down_del_num = root->left_son_ptr->TreeSize; if (root->tree_deleted) root->left_son_ptr->invalid_point_num = root->left_son_ptr->TreeSize; else root->left_son_ptr->invalid_point_num = root->left_son_ptr->down_del_num; root->left_son_ptr->need_push_down_to_left = true; root->left_son_ptr->need_push_down_to_right = true; root->need_push_down_to_left = false; } else { pthread_mutex_lock(&working_flag_mutex); root->left_son_ptr->tree_downsample_deleted |= root->tree_downsample_deleted; root->left_son_ptr->point_downsample_deleted |= root->tree_downsample_deleted; root->left_son_ptr->tree_deleted = root->tree_deleted || root->left_son_ptr->tree_downsample_deleted; root->left_son_ptr->point_deleted = root->left_son_ptr->tree_deleted || root->left_son_ptr->point_downsample_deleted; if (root->tree_downsample_deleted) root->left_son_ptr->down_del_num = root->left_son_ptr->TreeSize; if (root->tree_deleted) root->left_son_ptr->invalid_point_num = root->left_son_ptr->TreeSize; else root->left_son_ptr->invalid_point_num = root->left_son_ptr->down_del_num; root->left_son_ptr->need_push_down_to_left = true; root->left_son_ptr->need_push_down_to_right = true; if (rebuild_flag){ pthread_mutex_lock(&rebuild_logger_mutex_lock); Rebuild_Logger.push(operation); pthread_mutex_unlock(&rebuild_logger_mutex_lock); } root->need_push_down_to_left = false; pthread_mutex_unlock(&working_flag_mutex); } } if (root->need_push_down_to_right && root->right_son_ptr != nullptr){ if (Rebuild_Ptr == nullptr || *Rebuild_Ptr != root->right_son_ptr){ root->right_son_ptr->tree_downsample_deleted |= root->tree_downsample_deleted; root->right_son_ptr->point_downsample_deleted |= root->tree_downsample_deleted; root->right_son_ptr->tree_deleted = root->tree_deleted || root->right_son_ptr->tree_downsample_deleted; root->right_son_ptr->point_deleted = root->right_son_ptr->tree_deleted || root->right_son_ptr->point_downsample_deleted; if (root->tree_downsample_deleted) root->right_son_ptr->down_del_num = root->right_son_ptr->TreeSize; if (root->tree_deleted) root->right_son_ptr->invalid_point_num = root->right_son_ptr->TreeSize; else root->right_son_ptr->invalid_point_num = root->right_son_ptr->down_del_num; root->right_son_ptr->need_push_down_to_left = true; root->right_son_ptr->need_push_down_to_right = true; root->need_push_down_to_right = false; } else { pthread_mutex_lock(&working_flag_mutex); root->right_son_ptr->tree_downsample_deleted |= root->tree_downsample_deleted; root->right_son_ptr->point_downsample_deleted |= root->tree_downsample_deleted; root->right_son_ptr->tree_deleted = root->tree_deleted || root->right_son_ptr->tree_downsample_deleted; root->right_son_ptr->point_deleted = root->right_son_ptr->tree_deleted || root->right_son_ptr->point_downsample_deleted; if (root->tree_downsample_deleted) root->right_son_ptr->down_del_num = root->right_son_ptr->TreeSize; if (root->tree_deleted) root->right_son_ptr->invalid_point_num = root->right_son_ptr->TreeSize; else root->right_son_ptr->invalid_point_num = root->right_son_ptr->down_del_num; root->right_son_ptr->need_push_down_to_left = true; root->right_son_ptr->need_push_down_to_right = true; if (rebuild_flag){ pthread_mutex_lock(&rebuild_logger_mutex_lock); Rebuild_Logger.push(operation); pthread_mutex_unlock(&rebuild_logger_mutex_lock); } root->need_push_down_to_right = false; pthread_mutex_unlock(&working_flag_mutex); } } return; } template void KD_TREE::Update(KD_TREE_NODE * root){ KD_TREE_NODE * left_son_ptr = root->left_son_ptr; KD_TREE_NODE * right_son_ptr = root->right_son_ptr; float tmp_range_x[2] = {INFINITY, -INFINITY}; float tmp_range_y[2] = {INFINITY, -INFINITY}; float tmp_range_z[2] = {INFINITY, -INFINITY}; // Update Tree Size if (left_son_ptr != nullptr && right_son_ptr != nullptr){ root->TreeSize = left_son_ptr->TreeSize + right_son_ptr->TreeSize + 1; root->invalid_point_num = left_son_ptr->invalid_point_num + right_son_ptr->invalid_point_num + (root->point_deleted? 1:0); root->down_del_num = left_son_ptr->down_del_num + right_son_ptr->down_del_num + (root->point_downsample_deleted? 1:0); root->tree_downsample_deleted = left_son_ptr->tree_downsample_deleted & right_son_ptr->tree_downsample_deleted & root->point_downsample_deleted; root->tree_deleted = left_son_ptr->tree_deleted && right_son_ptr->tree_deleted && root->point_deleted; if (root->tree_deleted || (!left_son_ptr->tree_deleted && !right_son_ptr->tree_deleted && !root->point_deleted)){ tmp_range_x[0] = min(min(left_son_ptr->node_range_x[0],right_son_ptr->node_range_x[0]),root->point.x); tmp_range_x[1] = max(max(left_son_ptr->node_range_x[1],right_son_ptr->node_range_x[1]),root->point.x); tmp_range_y[0] = min(min(left_son_ptr->node_range_y[0],right_son_ptr->node_range_y[0]),root->point.y); tmp_range_y[1] = max(max(left_son_ptr->node_range_y[1],right_son_ptr->node_range_y[1]),root->point.y); tmp_range_z[0] = min(min(left_son_ptr->node_range_z[0],right_son_ptr->node_range_z[0]),root->point.z); tmp_range_z[1] = max(max(left_son_ptr->node_range_z[1],right_son_ptr->node_range_z[1]),root->point.z); } else { if (!left_son_ptr->tree_deleted){ tmp_range_x[0] = min(tmp_range_x[0], left_son_ptr->node_range_x[0]); tmp_range_x[1] = max(tmp_range_x[1], left_son_ptr->node_range_x[1]); tmp_range_y[0] = min(tmp_range_y[0], left_son_ptr->node_range_y[0]); tmp_range_y[1] = max(tmp_range_y[1], left_son_ptr->node_range_y[1]); tmp_range_z[0] = min(tmp_range_z[0], left_son_ptr->node_range_z[0]); tmp_range_z[1] = max(tmp_range_z[1], left_son_ptr->node_range_z[1]); } if (!right_son_ptr->tree_deleted){ tmp_range_x[0] = min(tmp_range_x[0], right_son_ptr->node_range_x[0]); tmp_range_x[1] = max(tmp_range_x[1], right_son_ptr->node_range_x[1]); tmp_range_y[0] = min(tmp_range_y[0], right_son_ptr->node_range_y[0]); tmp_range_y[1] = max(tmp_range_y[1], right_son_ptr->node_range_y[1]); tmp_range_z[0] = min(tmp_range_z[0], right_son_ptr->node_range_z[0]); tmp_range_z[1] = max(tmp_range_z[1], right_son_ptr->node_range_z[1]); } if (!root->point_deleted){ tmp_range_x[0] = min(tmp_range_x[0], root->point.x); tmp_range_x[1] = max(tmp_range_x[1], root->point.x); tmp_range_y[0] = min(tmp_range_y[0], root->point.y); tmp_range_y[1] = max(tmp_range_y[1], root->point.y); tmp_range_z[0] = min(tmp_range_z[0], root->point.z); tmp_range_z[1] = max(tmp_range_z[1], root->point.z); } } } else if (left_son_ptr != nullptr){ root->TreeSize = left_son_ptr->TreeSize + 1; root->invalid_point_num = left_son_ptr->invalid_point_num + (root->point_deleted?1:0); root->down_del_num = left_son_ptr->down_del_num + (root->point_downsample_deleted?1:0); root->tree_downsample_deleted = left_son_ptr->tree_downsample_deleted & root->point_downsample_deleted; root->tree_deleted = left_son_ptr->tree_deleted && root->point_deleted; if (root->tree_deleted || (!left_son_ptr->tree_deleted && !root->point_deleted)){ tmp_range_x[0] = min(left_son_ptr->node_range_x[0],root->point.x); tmp_range_x[1] = max(left_son_ptr->node_range_x[1],root->point.x); tmp_range_y[0] = min(left_son_ptr->node_range_y[0],root->point.y); tmp_range_y[1] = max(left_son_ptr->node_range_y[1],root->point.y); tmp_range_z[0] = min(left_son_ptr->node_range_z[0],root->point.z); tmp_range_z[1] = max(left_son_ptr->node_range_z[1],root->point.z); } else { if (!left_son_ptr->tree_deleted){ tmp_range_x[0] = min(tmp_range_x[0], left_son_ptr->node_range_x[0]); tmp_range_x[1] = max(tmp_range_x[1], left_son_ptr->node_range_x[1]); tmp_range_y[0] = min(tmp_range_y[0], left_son_ptr->node_range_y[0]); tmp_range_y[1] = max(tmp_range_y[1], left_son_ptr->node_range_y[1]); tmp_range_z[0] = min(tmp_range_z[0], left_son_ptr->node_range_z[0]); tmp_range_z[1] = max(tmp_range_z[1], left_son_ptr->node_range_z[1]); } if (!root->point_deleted){ tmp_range_x[0] = min(tmp_range_x[0], root->point.x); tmp_range_x[1] = max(tmp_range_x[1], root->point.x); tmp_range_y[0] = min(tmp_range_y[0], root->point.y); tmp_range_y[1] = max(tmp_range_y[1], root->point.y); tmp_range_z[0] = min(tmp_range_z[0], root->point.z); tmp_range_z[1] = max(tmp_range_z[1], root->point.z); } } } else if (right_son_ptr != nullptr){ root->TreeSize = right_son_ptr->TreeSize + 1; root->invalid_point_num = right_son_ptr->invalid_point_num + (root->point_deleted? 1:0); root->down_del_num = right_son_ptr->down_del_num + (root->point_downsample_deleted? 1:0); root->tree_downsample_deleted = right_son_ptr->tree_downsample_deleted & root->point_downsample_deleted; root->tree_deleted = right_son_ptr->tree_deleted && root->point_deleted; if (root->tree_deleted || (!right_son_ptr->tree_deleted && !root->point_deleted)){ tmp_range_x[0] = min(right_son_ptr->node_range_x[0],root->point.x); tmp_range_x[1] = max(right_son_ptr->node_range_x[1],root->point.x); tmp_range_y[0] = min(right_son_ptr->node_range_y[0],root->point.y); tmp_range_y[1] = max(right_son_ptr->node_range_y[1],root->point.y); tmp_range_z[0] = min(right_son_ptr->node_range_z[0],root->point.z); tmp_range_z[1] = max(right_son_ptr->node_range_z[1],root->point.z); } else { if (!right_son_ptr->tree_deleted){ tmp_range_x[0] = min(tmp_range_x[0], right_son_ptr->node_range_x[0]); tmp_range_x[1] = max(tmp_range_x[1], right_son_ptr->node_range_x[1]); tmp_range_y[0] = min(tmp_range_y[0], right_son_ptr->node_range_y[0]); tmp_range_y[1] = max(tmp_range_y[1], right_son_ptr->node_range_y[1]); tmp_range_z[0] = min(tmp_range_z[0], right_son_ptr->node_range_z[0]); tmp_range_z[1] = max(tmp_range_z[1], right_son_ptr->node_range_z[1]); } if (!root->point_deleted){ tmp_range_x[0] = min(tmp_range_x[0], root->point.x); tmp_range_x[1] = max(tmp_range_x[1], root->point.x); tmp_range_y[0] = min(tmp_range_y[0], root->point.y); tmp_range_y[1] = max(tmp_range_y[1], root->point.y); tmp_range_z[0] = min(tmp_range_z[0], root->point.z); tmp_range_z[1] = max(tmp_range_z[1], root->point.z); } } } else { root->TreeSize = 1; root->invalid_point_num = (root->point_deleted? 1:0); root->down_del_num = (root->point_downsample_deleted? 1:0); root->tree_downsample_deleted = root->point_downsample_deleted; root->tree_deleted = root->point_deleted; tmp_range_x[0] = root->point.x; tmp_range_x[1] = root->point.x; tmp_range_y[0] = root->point.y; tmp_range_y[1] = root->point.y; tmp_range_z[0] = root->point.z; tmp_range_z[1] = root->point.z; } memcpy(root->node_range_x,tmp_range_x,sizeof(tmp_range_x)); memcpy(root->node_range_y,tmp_range_y,sizeof(tmp_range_y)); memcpy(root->node_range_z,tmp_range_z,sizeof(tmp_range_z)); float x_L = (root->node_range_x[1] - root->node_range_x[0]) * 0.5; float y_L = (root->node_range_y[1] - root->node_range_y[0]) * 0.5; float z_L = (root->node_range_z[1] - root->node_range_z[0]) * 0.5; root->radius_sq = x_L*x_L + y_L * y_L + z_L * z_L; if (left_son_ptr != nullptr) left_son_ptr -> father_ptr = root; if (right_son_ptr != nullptr) right_son_ptr -> father_ptr = root; if (root == Root_Node && root->TreeSize > 3){ KD_TREE_NODE * son_ptr = root->left_son_ptr; if (son_ptr == nullptr) son_ptr = root->right_son_ptr; float tmp_bal = float(son_ptr->TreeSize) / (root->TreeSize-1); root->alpha_del = float(root->invalid_point_num)/ root->TreeSize; root->alpha_bal = (tmp_bal>=0.5-EPSS)?tmp_bal:1-tmp_bal; } return; } template void KD_TREE::checkCorrespondding(KD_TREE_NODE * root){ if (root == nullptr) return; if (!root->point_deleted) { ++num_nodes; const PointType &p = root->point; VOXEL_LOC voxel_by_point; float cube_center[3]; point2Voxel(p, voxel_by_point, cube_center); } checkCorrespondding(root->left_son_ptr); checkCorrespondding(root->right_son_ptr); return; } template void KD_TREE::flatten(KD_TREE_NODE * root, PointVector &Storage, delete_point_storage_set storage_type){ if (root == nullptr) return; Push_Down(root); if (!root->point_deleted) { Storage.push_back(root->point); } flatten(root->left_son_ptr, Storage, storage_type); flatten(root->right_son_ptr, Storage, storage_type); switch (storage_type) { case NOT_RECORD: break; case DELETE_POINTS_REC: if (root->point_deleted && !root->point_downsample_deleted) { Points_deleted.push_back(root->point); } break; case MULTI_THREAD_REC: if (root->point_deleted && !root->point_downsample_deleted) { Multithread_Points_deleted.push_back(root->point); } break; default: break; } return; } template void KD_TREE::delete_tree_nodes(KD_TREE_NODE ** root){ if (*root == nullptr) return; Push_Down(*root); delete_tree_nodes(&(*root)->left_son_ptr); delete_tree_nodes(&(*root)->right_son_ptr); pthread_mutex_destroy( &(*root)->push_down_mutex_lock); delete *root; *root = nullptr; return; } template void KD_TREE::node2Voxel(KD_TREE_NODE * root, VOXEL_LOC& voxel) { point2Voxel(root->point, voxel); } template void KD_TREE:: point2Voxel(const PointType& p, VOXEL_LOC& voxel) { //divide into voxel float loc_xyz[3]; point2Voxel(p, loc_xyz); voxel = VOXEL_LOC((int64_t)loc_xyz[0], (int64_t)loc_xyz[1], (int64_t)loc_xyz[2]); } template void KD_TREE:: point2Voxel(const PointType& p, float* loc_xyz, float* center_xyz) { point2Voxel(p, loc_xyz); voxelLoc2Center(loc_xyz, center_xyz); } template void KD_TREE:: point2Voxel(const PointType& p, float* loc_xyz) { //divide into voxel loc_xyz[0] = floor(p.x / downsample_size); loc_xyz[1] = floor(p.y / downsample_size); loc_xyz[2] = floor(p.z / downsample_size); } template void KD_TREE:: voxelLoc2Center(const float* loc_xyz, float* center_xyz) { center_xyz[0] = loc_xyz[0] * downsample_size + half_voxel_size; center_xyz[1] = loc_xyz[1] * downsample_size + half_voxel_size; center_xyz[2] = loc_xyz[2] * downsample_size + half_voxel_size; } template void KD_TREE:: point2Voxel(const PointType& p, VOXEL_LOC& voxel, float* center_cube) { //divide into voxel float loc_xyz[3]; point2Voxel(p, loc_xyz); voxel = VOXEL_LOC((int64_t)loc_xyz[0], (int64_t)loc_xyz[1], (int64_t)loc_xyz[2]); voxelLoc2Center(loc_xyz, center_cube); } template void KD_TREE::single_connect(KD_TREE_NODE * root) { if (root == nullptr) return; if (!root->point_deleted) { const PointType& p = root->point; VOXEL_LOC voxel; point2Voxel(p, voxel); voxel2node[voxel] = root; auto iter = voxel2info.find(voxel); if (iter == voxel2info.end() || !iter->second) { // VoxelInfo voxel_new(p); voxel2info[voxel] = new VoxelInfo(p); } } } template void KD_TREE::buildConnection(KD_TREE_NODE * root, const bool & traverse) { if (root == nullptr) return; // Push_Down(root); if (!root->point_deleted) { const PointType& p = root->point; float loc_xyz[3]; VOXEL_LOC voxel; point2Voxel(p, voxel); voxel2node[voxel] = root; auto iter = voxel2info.find(voxel); if (iter == voxel2info.end() || !iter->second) { voxel2info[voxel] = new VoxelInfo(p); } } if (!traverse) return; buildConnection(root->left_son_ptr); buildConnection(root->right_son_ptr); } template bool KD_TREE::same_point(PointType a, PointType b){ return (fabs(a.x-b.x) < EPSS && fabs(a.y-b.y) < EPSS && fabs(a.z-b.z) < EPSS ); } template float KD_TREE::calc_dist(PointType a, PointType b){ float dist = 0.0f; dist = (a.x-b.x)*(a.x-b.x) + (a.y-b.y)*(a.y-b.y) + (a.z-b.z)*(a.z-b.z); return dist; } template float KD_TREE::calc_dist(const PointType& a, const Eigen::Vector3f& b){ float dist = 0.0f; dist = (a.x-b(0))*(a.x-b(0)) + (a.y-b(1))*(a.y-b(1)) + (a.z-b(2))*(a.z-b(2)); return dist; } template float KD_TREE::calc_dist(const PointType& a, const float& x, const float& y, const float& z){ float dist = 0.0f; dist = (a.x-x)*(a.x-x) + (a.y-y)*(a.y-y) + (a.z-z)*(a.z-z); return dist; } template float KD_TREE::calc_dist(PointType a){ return sqrt(a.x * a.x + a.y * a.y + a.z * a.z); } template float KD_TREE::calc_box_dist(KD_TREE_NODE * node, PointType point){ if (node == nullptr) return INFINITY; float min_dist = 0.0; if (point.x < node->node_range_x[0]) min_dist += (point.x - node->node_range_x[0])*(point.x - node->node_range_x[0]); if (point.x > node->node_range_x[1]) min_dist += (point.x - node->node_range_x[1])*(point.x - node->node_range_x[1]); if (point.y < node->node_range_y[0]) min_dist += (point.y - node->node_range_y[0])*(point.y - node->node_range_y[0]); if (point.y > node->node_range_y[1]) min_dist += (point.y - node->node_range_y[1])*(point.y - node->node_range_y[1]); if (point.z < node->node_range_z[0]) min_dist += (point.z - node->node_range_z[0])*(point.z - node->node_range_z[0]); if (point.z > node->node_range_z[1]) min_dist += (point.z - node->node_range_z[1])*(point.z - node->node_range_z[1]); return min_dist; } template bool KD_TREE::point_cmp_x(PointType a, PointType b) { return a.x < b.x;} template bool KD_TREE::point_cmp_y(PointType a, PointType b) { return a.y < b.y;} template bool KD_TREE::point_cmp_z(PointType a, PointType b) { return a.z < b.z;} // manual queue template void MANUAL_Q::clear(){ head = 0; tail = 0; counter = 0; is_empty = true; return; } template void MANUAL_Q::pop(){ if (counter == 0) return; head ++; head %= Q_LEN; counter --; if (counter == 0) is_empty = true; return; } template T MANUAL_Q::front(){ return q[head]; } template T MANUAL_Q::back(){ return q[tail]; } template void MANUAL_Q::push(T op){ q[tail] = op; counter ++; if (is_empty) is_empty = false; tail ++; tail %= Q_LEN; } template bool MANUAL_Q::empty(){ return is_empty; } template int MANUAL_Q::size(){ return counter; } //template class VoxelInfo; //template class KD_TREE; //template class KD_TREE; //template class KD_TREE; template class KD_TREE; ================================================ FILE: include/ikd-Tree/ikd_Tree.h ================================================ #pragma once #include #include #include #include #include #include #include #include #include #include #include #include #include "../point_type.h" #include "voxel_map_util.hpp" #define EPSS 1e-6 #define Minimal_Unbalanced_Tree_Size 10 #define Multi_Thread_Rebuild_Point_Num 1500 #define DOWNSAMPLE_SWITCH true #define ForceRebuildPercentage 0.2 #define Q_LEN 1000000 using namespace std; struct BoxPointType{ float vertex_min[3]; float vertex_max[3]; }; enum operation_set {ADD_POINT, DELETE_POINT, DELETE_BOX, ADD_BOX, DOWNSAMPLE_DELETE, PUSH_DOWN}; enum delete_point_storage_set {NOT_RECORD, DELETE_POINTS_REC, MULTI_THREAD_REC}; template class MANUAL_Q{ private: int head = 0,tail = 0, counter = 0; T q[Q_LEN]; bool is_empty; public: void pop(); T front(); T back(); void clear(); void push(T op); bool empty(); int size(); }; struct SimpInfo { float s_xx = 0.0, s_yy = 0.0, s_zz = 0.0, s_xy = 0.0, s_xz = 0.0, s_yz = 0.0; Eigen::Vector3f normal, mean_xyz; int num_points = 0; }; class VoxelInfo { typedef pcl::PointXYZINormal PointType; public: VoxelInfo() :is_init(false) , flag_updating(false) { plane = new Plane; // pthread_mutex_init(&updating_mutex_lock, NULL); } // initialize with single point VoxelInfo(const PointType & p); void init(); void updateWithSingle(const PointType & p); void updateWithSingle(const PointType & p, const Eigen::Matrix3f & cov); void updateWithCloud(); void updateSumAndMean(const PointType & p); void updatePlaneUncertainty(const PointType & p, const Eigen::Matrix3f & cov); void updateNormal(const PointType & p); bool checkPCA(); void updatePlaneCov(); void getSimpInfo(SimpInfo& out); bool is_init; float s_xx = 0.0, s_yy = 0.0, s_zz = 0.0, s_xy = 0.0, s_xz = 0.0, s_yz = 0.0; float mean_x = 0.0, mean_y = 0.0, mean_z = 0.0; int num_points = 0; Eigen::Vector3f normal; Plane * plane; vector points; mutex updating_mutex_lock; bool flag_updating = false; }; template class KD_TREE{ public: using PointVector = vector>; using Ptr = shared_ptr>; using VoxelInfoPtr = VoxelInfo*; struct KD_TREE_NODE{ PointType point; uint8_t division_axis; int TreeSize = 1; int invalid_point_num = 0; int down_del_num = 0; bool point_deleted = false; bool tree_deleted = false; bool point_downsample_deleted = false; bool tree_downsample_deleted = false; bool need_push_down_to_left = false; bool need_push_down_to_right = false; bool working_flag = false; float radius_sq; pthread_mutex_t push_down_mutex_lock; float node_range_x[2], node_range_y[2], node_range_z[2]; KD_TREE_NODE *left_son_ptr = nullptr; KD_TREE_NODE *right_son_ptr = nullptr; KD_TREE_NODE *father_ptr = nullptr; // For paper data record float alpha_del; float alpha_bal; }; struct Operation_Logger_Type{ PointType point; BoxPointType boxpoint; bool tree_deleted, tree_downsample_deleted; operation_set op; }; struct PointType_Voxel_CMP{ PointType point; float dist = 0.0; VoxelInfoPtr info = nullptr; PointType_Voxel_CMP (PointType p = PointType(), VoxelInfoPtr n = nullptr, const float& d = INFINITY){ this->point = p; this->dist = d; this->info = n; }; bool operator < (const PointType_Voxel_CMP &a)const{ if (fabs(dist - a.dist) < 1e-10) return point.x < a.point.x; else return dist < a.dist; } }; class MANUAL_VOXEL_HEAP{ public: MANUAL_VOXEL_HEAP(int max_capacity = 100){ cap = max_capacity; heap = new PointType_Voxel_CMP[max_capacity]; heap_size = 0; } ~MANUAL_VOXEL_HEAP(){ delete[] heap;} void pop(){ if (heap_size == 0) return; heap[0] = heap[heap_size-1]; heap_size--; MoveDown(0); return; } PointType_Voxel_CMP top(){ return heap[0];} void push(PointType_Voxel_CMP point){ if (heap_size >= cap) return; heap[heap_size] = point; FloatUp(heap_size); heap_size++; return; } int size(){ return heap_size;} void clear(){ heap_size = 0;} private: int heap_size = 0; int cap = 0; PointType_Voxel_CMP * heap; void MoveDown(int heap_index){ int l = heap_index * 2 + 1; PointType_Voxel_CMP tmp = heap[heap_index]; while (l < heap_size){ if (l + 1 < heap_size && heap[l] < heap[l+1]) l++; if (tmp < heap[l]){ heap[heap_index] = heap[l]; heap_index = l; l = heap_index * 2 + 1; } else break; } heap[heap_index] = tmp; return; } void FloatUp(int heap_index){ int ancestor = (heap_index-1)/2; PointType_Voxel_CMP tmp = heap[heap_index]; while (heap_index > 0){ if (heap[ancestor] < tmp){ heap[heap_index] = heap[ancestor]; heap_index = ancestor; ancestor = (heap_index-1)/2; } else break; } heap[heap_index] = tmp; return; } }; struct PointType_CMP{ PointType point; float dist = 0.0; KD_TREE_NODE* node = nullptr; // PointType_CMP (PointType p = PointType(), float d = INFINITY){ // this->point = p; // this->dist = d; // }; PointType_CMP (PointType p = PointType(), KD_TREE_NODE* n = nullptr, const float& d = INFINITY){ this->point = p; this->dist = d; this->node = n; }; bool operator < (const PointType_CMP &a)const{ if (fabs(dist - a.dist) < 1e-10) return point.x < a.point.x; else return dist < a.dist; } }; class MANUAL_HEAP{ public: MANUAL_HEAP(int max_capacity = 100){ cap = max_capacity; heap = new PointType_CMP[max_capacity]; heap_size = 0; } ~MANUAL_HEAP(){ delete[] heap;} void pop(){ if (heap_size == 0) return; heap[0] = heap[heap_size-1]; heap_size--; MoveDown(0); return; } PointType_CMP top(){ return heap[0];} void push(PointType_CMP point){ if (heap_size >= cap) return; heap[heap_size] = point; FloatUp(heap_size); heap_size++; return; } int size(){ return heap_size;} void clear(){ heap_size = 0;} private: int heap_size = 0; int cap = 0; PointType_CMP * heap; void MoveDown(int heap_index){ int l = heap_index * 2 + 1; PointType_CMP tmp = heap[heap_index]; while (l < heap_size){ if (l + 1 < heap_size && heap[l] < heap[l+1]) l++; if (tmp < heap[l]){ heap[heap_index] = heap[l]; heap_index = l; l = heap_index * 2 + 1; } else break; } heap[heap_index] = tmp; return; } void FloatUp(int heap_index){ int ancestor = (heap_index-1)/2; PointType_CMP tmp = heap[heap_index]; while (heap_index > 0){ if (heap[ancestor] < tmp){ heap[heap_index] = heap[ancestor]; heap_index = ancestor; ancestor = (heap_index-1)/2; } else break; } heap[heap_index] = tmp; return; } }; private: // Multi-thread Tree Rebuild bool termination_flag = false; bool rebuild_flag = false; pthread_t rebuild_thread; pthread_mutex_t termination_flag_mutex_lock, rebuild_ptr_mutex_lock, working_flag_mutex, search_flag_mutex; pthread_mutex_t rebuild_logger_mutex_lock, points_deleted_rebuild_mutex_lock; // queue Rebuild_Logger; MANUAL_Q Rebuild_Logger; PointVector Rebuild_PCL_Storage; KD_TREE_NODE ** Rebuild_Ptr = nullptr; int search_mutex_counter = 0; static void * multi_thread_ptr(void *arg); void multi_thread_rebuild(); void start_thread(); void stop_thread(); void run_operation(KD_TREE_NODE ** root, Operation_Logger_Type operation); // KD Tree Functions and augmented variables int Treesize_tmp = 0, Validnum_tmp = 0; float alpha_bal_tmp = 0.5, alpha_del_tmp = 0.0; float delete_criterion_param = 0.5f; float balance_criterion_param = 0.7f; float downsample_size = 0.2f; bool Delete_Storage_Disabled = false; KD_TREE_NODE * STATIC_ROOT_NODE = nullptr; PointVector Points_deleted; PointVector Downsample_Storage; PointVector Multithread_Points_deleted; void InitTreeNode(KD_TREE_NODE * root); void Test_Lock_States(KD_TREE_NODE *root); void BuildTree(KD_TREE_NODE ** root, int l, int r, PointVector & Storage); void Rebuild(KD_TREE_NODE ** root); int Delete_by_range(KD_TREE_NODE ** root, BoxPointType boxpoint, bool allow_rebuild, bool is_downsample); void Delete_by_point(KD_TREE_NODE ** root, PointType point, bool allow_rebuild); void Add_by_point(KD_TREE_NODE ** root, PointType point, bool allow_rebuild, int father_axis); void Add_by_range(KD_TREE_NODE ** root, BoxPointType boxpoint, bool allow_rebuild); void Search(KD_TREE_NODE * root, int k_nearest, PointType point, MANUAL_HEAP &q, double max_dist);//priority_queue void Search_by_range(KD_TREE_NODE *root, BoxPointType boxpoint, PointVector &Storage); void Search_by_radius(KD_TREE_NODE *root, PointType point, float radius, PointVector &Storage); bool Criterion_Check(KD_TREE_NODE * root); void Push_Down(KD_TREE_NODE * root); void Update(KD_TREE_NODE * root); void delete_tree_nodes(KD_TREE_NODE ** root); void downsample(KD_TREE_NODE ** root); bool same_point(PointType a, PointType b); float calc_dist(PointType a, PointType b); float calc_dist(const PointType& a, const Eigen::Vector3f& b); float calc_dist(const PointType& a, const float& x, const float& y, const float& z); float calc_dist(PointType a); float calc_box_dist(KD_TREE_NODE * node, PointType point); static bool point_cmp_x(PointType a, PointType b); static bool point_cmp_y(PointType a, PointType b); static bool point_cmp_z(PointType a, PointType b); void buildConnection(KD_TREE_NODE * root, const bool & traverse = true); //build connection between ikdtree nodes and voxels void single_connect(KD_TREE_NODE * root); void point2Voxel(const PointType& p, VOXEL_LOC& voxel); void point2Voxel(const PointType& p, float* loc_xyz); void point2Voxel(const PointType& p, float* loc_xyz, float* center_xyz); void point2Voxel(const PointType& p, VOXEL_LOC& voxel, float* center_cube); void voxelIndexNode(const PointType& p, VOXEL_LOC& voxel); void node2Voxel(KD_TREE_NODE * root, VOXEL_LOC& voxel); void insideVoxelDownsample(PointVector& point_cloud); void voxelLoc2Center(const float* loc_xyz, float* center_xyz); void addNearVoxelsLoc(const PointType& point, const float* voxel_center, std::vector& voxels); public: KD_TREE(float delete_param = 0.5, float balance_param = 0.6 , float box_length = 0.2); ~KD_TREE(); void Set_delete_criterion_param(float delete_param); void Set_balance_criterion_param(float balance_param); void set_downsample_param(float box_length); void InitializeKDTree(float delete_param = 0.5, float balance_param = 0.7, float box_length = 0.2); int size(); int validnum(); void root_alpha(float &alpha_bal, float &alpha_del); void Build(PointVector point_cloud); void Nearest_Search(PointType point, int k_nearest, PointVector &Nearest_Points, vector & Point_Distance, double max_dist = INFINITY); void Nearest_Search(PointType point, int k_nearest, PointVector &Nearest_Points, vector & Point_Distance, vector& voxels, double max_dist = INFINITY); void Box_Search(const BoxPointType &Box_of_Point, PointVector &Storage); void Radius_Search(PointType point, const float radius, PointVector &Storage); int Add_Points(PointVector & PointToAdd, bool downsample_on); void Add_Point_Boxes(vector & BoxPoints); void Delete_Points(PointVector & PointToDel); int Delete_Point_Boxes(vector & BoxPoints); void flatten(KD_TREE_NODE * root, PointVector &Storage, delete_point_storage_set storage_type); void checkCorrespondding(KD_TREE_NODE * root); // int checkCorresponddingVoxelAndNode(KD_TREE_NODE * root); void acquire_removed_points(PointVector & removed_points); // void updateNodeInfo(KD_TREE_NODE* node, const PointType& point); void Voxel_Search(const PointType& point, int k_nearest, PointVector &Nearest_Points, vector& nodes); void Voxel_Search_test(const PointType& point, int k_nearest, PointVector &Nearest_Points, vector & Point_Distance, vector& voxels); void updateVoxelInfo(const PointType& point); void updateVoxelInfo(const PointType& point, const Eigen::Matrix3f & cov); //voxel map // unordered_map voxel_map; unordered_map voxel2node; unordered_map voxel2info; // unordered_map node2voxel; void findVoxel(const PointType& p); int max_layer, layer, max_cov_points_size, max_points_size; vector layer_point_size; float planer_threshold; float voxel_size; double max_voxel_size, min_eigen_value, half_voxel_size, quater_voxel_size; int voxel_conflict = 0; int max_voxel_points_size; BoxPointType tree_range(); PointVector PCL_Storage; KD_TREE_NODE * Root_Node = nullptr; int max_queue_size = 0; //check corresponding int num_node2_voxel_null = 0, num_voxel2node_null = 0; int num_nodes = 0; int voxel2info_null = 0; int num_same_info = 0, num_diff_info = 0; int num_same_voxel2node = 0; int num_node2voxel_valid = 0, voxel2info_valid = 0; int num_same_voxel_by_pointAndnode = 0; }; ================================================ FILE: include/matplotlibcpp.h ================================================ #pragma once // Python headers must be included before any system headers, since // they define _POSIX_C_SOURCE #include #include #include #include #include #include #include #include #include // requires c++11 support #include #ifndef WITHOUT_NUMPY # define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION # include # ifdef WITH_OPENCV # include # endif // WITH_OPENCV /* * A bunch of constants were removed in OpenCV 4 in favour of enum classes, so * define the ones we need here. */ # if CV_MAJOR_VERSION > 3 # define CV_BGR2RGB cv::COLOR_BGR2RGB # define CV_BGRA2RGBA cv::COLOR_BGRA2RGBA # endif #endif // WITHOUT_NUMPY #if PY_MAJOR_VERSION >= 3 # define PyString_FromString PyUnicode_FromString # define PyInt_FromLong PyLong_FromLong # define PyString_FromString PyUnicode_FromString #endif namespace matplotlibcpp { namespace detail { static std::string s_backend; struct _interpreter { PyObject* s_python_function_arrow; PyObject *s_python_function_show; PyObject *s_python_function_close; PyObject *s_python_function_draw; PyObject *s_python_function_pause; PyObject *s_python_function_save; PyObject *s_python_function_figure; PyObject *s_python_function_fignum_exists; PyObject *s_python_function_plot; PyObject *s_python_function_quiver; PyObject* s_python_function_contour; PyObject *s_python_function_semilogx; PyObject *s_python_function_semilogy; PyObject *s_python_function_loglog; PyObject *s_python_function_fill; PyObject *s_python_function_fill_between; PyObject *s_python_function_hist; PyObject *s_python_function_imshow; PyObject *s_python_function_scatter; PyObject *s_python_function_boxplot; PyObject *s_python_function_subplot; PyObject *s_python_function_subplot2grid; PyObject *s_python_function_legend; PyObject *s_python_function_xlim; PyObject *s_python_function_ion; PyObject *s_python_function_ginput; PyObject *s_python_function_ylim; PyObject *s_python_function_title; PyObject *s_python_function_axis; PyObject *s_python_function_axvline; PyObject *s_python_function_axvspan; PyObject *s_python_function_xlabel; PyObject *s_python_function_ylabel; PyObject *s_python_function_gca; PyObject *s_python_function_xticks; PyObject *s_python_function_yticks; PyObject* s_python_function_margins; PyObject *s_python_function_tick_params; PyObject *s_python_function_grid; PyObject* s_python_function_cla; PyObject *s_python_function_clf; PyObject *s_python_function_errorbar; PyObject *s_python_function_annotate; PyObject *s_python_function_tight_layout; PyObject *s_python_colormap; PyObject *s_python_empty_tuple; PyObject *s_python_function_stem; PyObject *s_python_function_xkcd; PyObject *s_python_function_text; PyObject *s_python_function_suptitle; PyObject *s_python_function_bar; PyObject *s_python_function_colorbar; PyObject *s_python_function_subplots_adjust; /* For now, _interpreter is implemented as a singleton since its currently not possible to have multiple independent embedded python interpreters without patching the python source code or starting a separate process for each. http://bytes.com/topic/python/answers/793370-multiple-independent-python-interpreters-c-c-program */ static _interpreter& get() { static _interpreter ctx; return ctx; } PyObject* safe_import(PyObject* module, std::string fname) { PyObject* fn = PyObject_GetAttrString(module, fname.c_str()); if (!fn) throw std::runtime_error(std::string("Couldn't find required function: ") + fname); if (!PyFunction_Check(fn)) throw std::runtime_error(fname + std::string(" is unexpectedly not a PyFunction.")); return fn; } private: #ifndef WITHOUT_NUMPY # if PY_MAJOR_VERSION >= 3 void *import_numpy() { import_array(); // initialize C-API return NULL; } # else void import_numpy() { import_array(); // initialize C-API } # endif #endif _interpreter() { // optional but recommended #if PY_MAJOR_VERSION >= 3 wchar_t name[] = L"plotting"; #else char name[] = "plotting"; #endif Py_SetProgramName(name); Py_Initialize(); #ifndef WITHOUT_NUMPY import_numpy(); // initialize numpy C-API #endif PyObject* matplotlibname = PyString_FromString("matplotlib"); PyObject* pyplotname = PyString_FromString("matplotlib.pyplot"); PyObject* cmname = PyString_FromString("matplotlib.cm"); PyObject* pylabname = PyString_FromString("pylab"); if (!pyplotname || !pylabname || !matplotlibname || !cmname) { throw std::runtime_error("couldnt create string"); } PyObject* matplotlib = PyImport_Import(matplotlibname); Py_DECREF(matplotlibname); if (!matplotlib) { PyErr_Print(); throw std::runtime_error("Error loading module matplotlib!"); } // matplotlib.use() must be called *before* pylab, matplotlib.pyplot, // or matplotlib.backends is imported for the first time if (!s_backend.empty()) { PyObject_CallMethod(matplotlib, const_cast("use"), const_cast("s"), s_backend.c_str()); } PyObject* pymod = PyImport_Import(pyplotname); Py_DECREF(pyplotname); if (!pymod) { throw std::runtime_error("Error loading module matplotlib.pyplot!"); } s_python_colormap = PyImport_Import(cmname); Py_DECREF(cmname); if (!s_python_colormap) { throw std::runtime_error("Error loading module matplotlib.cm!"); } PyObject* pylabmod = PyImport_Import(pylabname); Py_DECREF(pylabname); if (!pylabmod) { throw std::runtime_error("Error loading module pylab!"); } s_python_function_arrow = safe_import(pymod, "arrow"); s_python_function_show = safe_import(pymod, "show"); s_python_function_close = safe_import(pymod, "close"); s_python_function_draw = safe_import(pymod, "draw"); s_python_function_pause = safe_import(pymod, "pause"); s_python_function_figure = safe_import(pymod, "figure"); s_python_function_fignum_exists = safe_import(pymod, "fignum_exists"); s_python_function_plot = safe_import(pymod, "plot"); s_python_function_quiver = safe_import(pymod, "quiver"); s_python_function_contour = safe_import(pymod, "contour"); s_python_function_semilogx = safe_import(pymod, "semilogx"); s_python_function_semilogy = safe_import(pymod, "semilogy"); s_python_function_loglog = safe_import(pymod, "loglog"); s_python_function_fill = safe_import(pymod, "fill"); s_python_function_fill_between = safe_import(pymod, "fill_between"); s_python_function_hist = safe_import(pymod,"hist"); s_python_function_scatter = safe_import(pymod,"scatter"); s_python_function_boxplot = safe_import(pymod,"boxplot"); s_python_function_subplot = safe_import(pymod, "subplot"); s_python_function_subplot2grid = safe_import(pymod, "subplot2grid"); s_python_function_legend = safe_import(pymod, "legend"); s_python_function_ylim = safe_import(pymod, "ylim"); s_python_function_title = safe_import(pymod, "title"); s_python_function_axis = safe_import(pymod, "axis"); s_python_function_axvline = safe_import(pymod, "axvline"); s_python_function_axvspan = safe_import(pymod, "axvspan"); s_python_function_xlabel = safe_import(pymod, "xlabel"); s_python_function_ylabel = safe_import(pymod, "ylabel"); s_python_function_gca = safe_import(pymod, "gca"); s_python_function_xticks = safe_import(pymod, "xticks"); s_python_function_yticks = safe_import(pymod, "yticks"); s_python_function_margins = safe_import(pymod, "margins"); s_python_function_tick_params = safe_import(pymod, "tick_params"); s_python_function_grid = safe_import(pymod, "grid"); s_python_function_xlim = safe_import(pymod, "xlim"); s_python_function_ion = safe_import(pymod, "ion"); s_python_function_ginput = safe_import(pymod, "ginput"); s_python_function_save = safe_import(pylabmod, "savefig"); s_python_function_annotate = safe_import(pymod,"annotate"); s_python_function_cla = safe_import(pymod, "cla"); s_python_function_clf = safe_import(pymod, "clf"); s_python_function_errorbar = safe_import(pymod, "errorbar"); s_python_function_tight_layout = safe_import(pymod, "tight_layout"); s_python_function_stem = safe_import(pymod, "stem"); s_python_function_xkcd = safe_import(pymod, "xkcd"); s_python_function_text = safe_import(pymod, "text"); s_python_function_suptitle = safe_import(pymod, "suptitle"); s_python_function_bar = safe_import(pymod,"bar"); s_python_function_colorbar = PyObject_GetAttrString(pymod, "colorbar"); s_python_function_subplots_adjust = safe_import(pymod,"subplots_adjust"); #ifndef WITHOUT_NUMPY s_python_function_imshow = safe_import(pymod, "imshow"); #endif s_python_empty_tuple = PyTuple_New(0); } ~_interpreter() { Py_Finalize(); } }; } // end namespace detail /// Select the backend /// /// **NOTE:** This must be called before the first plot command to have /// any effect. /// /// Mainly useful to select the non-interactive 'Agg' backend when running /// matplotlibcpp in headless mode, for example on a machine with no display. /// /// See also: https://matplotlib.org/2.0.2/api/matplotlib_configuration_api.html#matplotlib.use inline void backend(const std::string& name) { detail::s_backend = name; } inline bool annotate(std::string annotation, double x, double y) { detail::_interpreter::get(); PyObject * xy = PyTuple_New(2); PyObject * str = PyString_FromString(annotation.c_str()); PyTuple_SetItem(xy,0,PyFloat_FromDouble(x)); PyTuple_SetItem(xy,1,PyFloat_FromDouble(y)); PyObject* kwargs = PyDict_New(); PyDict_SetItemString(kwargs, "xy", xy); PyObject* args = PyTuple_New(1); PyTuple_SetItem(args, 0, str); PyObject* res = PyObject_Call(detail::_interpreter::get().s_python_function_annotate, args, kwargs); Py_DECREF(args); Py_DECREF(kwargs); if(res) Py_DECREF(res); return res; } namespace detail { #ifndef WITHOUT_NUMPY // Type selector for numpy array conversion template struct select_npy_type { const static NPY_TYPES type = NPY_NOTYPE; }; //Default template <> struct select_npy_type { const static NPY_TYPES type = NPY_DOUBLE; }; template <> struct select_npy_type { const static NPY_TYPES type = NPY_FLOAT; }; template <> struct select_npy_type { const static NPY_TYPES type = NPY_BOOL; }; template <> struct select_npy_type { const static NPY_TYPES type = NPY_INT8; }; template <> struct select_npy_type { const static NPY_TYPES type = NPY_SHORT; }; template <> struct select_npy_type { const static NPY_TYPES type = NPY_INT; }; template <> struct select_npy_type { const static NPY_TYPES type = NPY_INT64; }; template <> struct select_npy_type { const static NPY_TYPES type = NPY_UINT8; }; template <> struct select_npy_type { const static NPY_TYPES type = NPY_USHORT; }; template <> struct select_npy_type { const static NPY_TYPES type = NPY_ULONG; }; template <> struct select_npy_type { const static NPY_TYPES type = NPY_UINT64; }; // Sanity checks; comment them out or change the numpy type below if you're compiling on // a platform where they don't apply static_assert(sizeof(long long) == 8); template <> struct select_npy_type { const static NPY_TYPES type = NPY_INT64; }; static_assert(sizeof(unsigned long long) == 8); template <> struct select_npy_type { const static NPY_TYPES type = NPY_UINT64; }; // TODO: add int, long, etc. template PyObject* get_array(const std::vector& v) { npy_intp vsize = v.size(); NPY_TYPES type = select_npy_type::type; if (type == NPY_NOTYPE) { size_t memsize = v.size()*sizeof(double); double* dp = static_cast(::malloc(memsize)); for (size_t i=0; i(varray), NPY_ARRAY_OWNDATA); return varray; } PyObject* varray = PyArray_SimpleNewFromData(1, &vsize, type, (void*)(v.data())); return varray; } template PyObject* get_2darray(const std::vector<::std::vector>& v) { if (v.size() < 1) throw std::runtime_error("get_2d_array v too small"); npy_intp vsize[2] = {static_cast(v.size()), static_cast(v[0].size())}; PyArrayObject *varray = (PyArrayObject *)PyArray_SimpleNew(2, vsize, NPY_DOUBLE); double *vd_begin = static_cast(PyArray_DATA(varray)); for (const ::std::vector &v_row : v) { if (v_row.size() != static_cast(vsize[1])) throw std::runtime_error("Missmatched array size"); std::copy(v_row.begin(), v_row.end(), vd_begin); vd_begin += vsize[1]; } return reinterpret_cast(varray); } #else // fallback if we don't have numpy: copy every element of the given vector template PyObject* get_array(const std::vector& v) { PyObject* list = PyList_New(v.size()); for(size_t i = 0; i < v.size(); ++i) { PyList_SetItem(list, i, PyFloat_FromDouble(v.at(i))); } return list; } #endif // WITHOUT_NUMPY // sometimes, for labels and such, we need string arrays inline PyObject * get_array(const std::vector& strings) { PyObject* list = PyList_New(strings.size()); for (std::size_t i = 0; i < strings.size(); ++i) { PyList_SetItem(list, i, PyString_FromString(strings[i].c_str())); } return list; } // not all matplotlib need 2d arrays, some prefer lists of lists template PyObject* get_listlist(const std::vector>& ll) { PyObject* listlist = PyList_New(ll.size()); for (std::size_t i = 0; i < ll.size(); ++i) { PyList_SetItem(listlist, i, get_array(ll[i])); } return listlist; } } // namespace detail /// Plot a line through the given x and y data points.. /// /// See: https://matplotlib.org/3.2.1/api/_as_gen/matplotlib.pyplot.plot.html template bool plot(const std::vector &x, const std::vector &y, const std::map& keywords) { assert(x.size() == y.size()); detail::_interpreter::get(); // using numpy arrays PyObject* xarray = detail::get_array(x); PyObject* yarray = detail::get_array(y); // construct positional args PyObject* args = PyTuple_New(2); PyTuple_SetItem(args, 0, xarray); PyTuple_SetItem(args, 1, yarray); // construct keyword args PyObject* kwargs = PyDict_New(); for(std::map::const_iterator it = keywords.begin(); it != keywords.end(); ++it) { PyDict_SetItemString(kwargs, it->first.c_str(), PyString_FromString(it->second.c_str())); } PyObject* res = PyObject_Call(detail::_interpreter::get().s_python_function_plot, args, kwargs); Py_DECREF(args); Py_DECREF(kwargs); if(res) Py_DECREF(res); return res; } // TODO - it should be possible to make this work by implementing // a non-numpy alternative for `detail::get_2darray()`. #ifndef WITHOUT_NUMPY template void plot_surface(const std::vector<::std::vector> &x, const std::vector<::std::vector> &y, const std::vector<::std::vector> &z, const std::map &keywords = std::map()) { detail::_interpreter::get(); // We lazily load the modules here the first time this function is called // because I'm not sure that we can assume "matplotlib installed" implies // "mpl_toolkits installed" on all platforms, and we don't want to require // it for people who don't need 3d plots. static PyObject *mpl_toolkitsmod = nullptr, *axis3dmod = nullptr; if (!mpl_toolkitsmod) { detail::_interpreter::get(); PyObject* mpl_toolkits = PyString_FromString("mpl_toolkits"); PyObject* axis3d = PyString_FromString("mpl_toolkits.mplot3d"); if (!mpl_toolkits || !axis3d) { throw std::runtime_error("couldnt create string"); } mpl_toolkitsmod = PyImport_Import(mpl_toolkits); Py_DECREF(mpl_toolkits); if (!mpl_toolkitsmod) { throw std::runtime_error("Error loading module mpl_toolkits!"); } axis3dmod = PyImport_Import(axis3d); Py_DECREF(axis3d); if (!axis3dmod) { throw std::runtime_error("Error loading module mpl_toolkits.mplot3d!"); } } assert(x.size() == y.size()); assert(y.size() == z.size()); // using numpy arrays PyObject *xarray = detail::get_2darray(x); PyObject *yarray = detail::get_2darray(y); PyObject *zarray = detail::get_2darray(z); // construct positional args PyObject *args = PyTuple_New(3); PyTuple_SetItem(args, 0, xarray); PyTuple_SetItem(args, 1, yarray); PyTuple_SetItem(args, 2, zarray); // Build up the kw args. PyObject *kwargs = PyDict_New(); PyDict_SetItemString(kwargs, "rstride", PyInt_FromLong(1)); PyDict_SetItemString(kwargs, "cstride", PyInt_FromLong(1)); PyObject *python_colormap_coolwarm = PyObject_GetAttrString( detail::_interpreter::get().s_python_colormap, "coolwarm"); PyDict_SetItemString(kwargs, "cmap", python_colormap_coolwarm); for (std::map::const_iterator it = keywords.begin(); it != keywords.end(); ++it) { PyDict_SetItemString(kwargs, it->first.c_str(), PyString_FromString(it->second.c_str())); } PyObject *fig = PyObject_CallObject(detail::_interpreter::get().s_python_function_figure, detail::_interpreter::get().s_python_empty_tuple); if (!fig) throw std::runtime_error("Call to figure() failed."); PyObject *gca_kwargs = PyDict_New(); PyDict_SetItemString(gca_kwargs, "projection", PyString_FromString("3d")); PyObject *gca = PyObject_GetAttrString(fig, "gca"); if (!gca) throw std::runtime_error("No gca"); Py_INCREF(gca); PyObject *axis = PyObject_Call( gca, detail::_interpreter::get().s_python_empty_tuple, gca_kwargs); if (!axis) throw std::runtime_error("No axis"); Py_INCREF(axis); Py_DECREF(gca); Py_DECREF(gca_kwargs); PyObject *plot_surface = PyObject_GetAttrString(axis, "plot_surface"); if (!plot_surface) throw std::runtime_error("No surface"); Py_INCREF(plot_surface); PyObject *res = PyObject_Call(plot_surface, args, kwargs); if (!res) throw std::runtime_error("failed surface"); Py_DECREF(plot_surface); Py_DECREF(axis); Py_DECREF(args); Py_DECREF(kwargs); if (res) Py_DECREF(res); } #endif // WITHOUT_NUMPY template void plot3(const std::vector &x, const std::vector &y, const std::vector &z, const std::map &keywords = std::map()) { detail::_interpreter::get(); // Same as with plot_surface: We lazily load the modules here the first time // this function is called because I'm not sure that we can assume "matplotlib // installed" implies "mpl_toolkits installed" on all platforms, and we don't // want to require it for people who don't need 3d plots. static PyObject *mpl_toolkitsmod = nullptr, *axis3dmod = nullptr; if (!mpl_toolkitsmod) { detail::_interpreter::get(); PyObject* mpl_toolkits = PyString_FromString("mpl_toolkits"); PyObject* axis3d = PyString_FromString("mpl_toolkits.mplot3d"); if (!mpl_toolkits || !axis3d) { throw std::runtime_error("couldnt create string"); } mpl_toolkitsmod = PyImport_Import(mpl_toolkits); Py_DECREF(mpl_toolkits); if (!mpl_toolkitsmod) { throw std::runtime_error("Error loading module mpl_toolkits!"); } axis3dmod = PyImport_Import(axis3d); Py_DECREF(axis3d); if (!axis3dmod) { throw std::runtime_error("Error loading module mpl_toolkits.mplot3d!"); } } assert(x.size() == y.size()); assert(y.size() == z.size()); PyObject *xarray = detail::get_array(x); PyObject *yarray = detail::get_array(y); PyObject *zarray = detail::get_array(z); // construct positional args PyObject *args = PyTuple_New(3); PyTuple_SetItem(args, 0, xarray); PyTuple_SetItem(args, 1, yarray); PyTuple_SetItem(args, 2, zarray); // Build up the kw args. PyObject *kwargs = PyDict_New(); for (std::map::const_iterator it = keywords.begin(); it != keywords.end(); ++it) { PyDict_SetItemString(kwargs, it->first.c_str(), PyString_FromString(it->second.c_str())); } PyObject *fig = PyObject_CallObject(detail::_interpreter::get().s_python_function_figure, detail::_interpreter::get().s_python_empty_tuple); if (!fig) throw std::runtime_error("Call to figure() failed."); PyObject *gca_kwargs = PyDict_New(); PyDict_SetItemString(gca_kwargs, "projection", PyString_FromString("3d")); PyObject *gca = PyObject_GetAttrString(fig, "gca"); if (!gca) throw std::runtime_error("No gca"); Py_INCREF(gca); PyObject *axis = PyObject_Call( gca, detail::_interpreter::get().s_python_empty_tuple, gca_kwargs); if (!axis) throw std::runtime_error("No axis"); Py_INCREF(axis); Py_DECREF(gca); Py_DECREF(gca_kwargs); PyObject *plot3 = PyObject_GetAttrString(axis, "plot"); if (!plot3) throw std::runtime_error("No 3D line plot"); Py_INCREF(plot3); PyObject *res = PyObject_Call(plot3, args, kwargs); if (!res) throw std::runtime_error("Failed 3D line plot"); Py_DECREF(plot3); Py_DECREF(axis); Py_DECREF(args); Py_DECREF(kwargs); if (res) Py_DECREF(res); } template bool stem(const std::vector &x, const std::vector &y, const std::map& keywords) { assert(x.size() == y.size()); detail::_interpreter::get(); // using numpy arrays PyObject* xarray = detail::get_array(x); PyObject* yarray = detail::get_array(y); // construct positional args PyObject* args = PyTuple_New(2); PyTuple_SetItem(args, 0, xarray); PyTuple_SetItem(args, 1, yarray); // construct keyword args PyObject* kwargs = PyDict_New(); for (std::map::const_iterator it = keywords.begin(); it != keywords.end(); ++it) { PyDict_SetItemString(kwargs, it->first.c_str(), PyString_FromString(it->second.c_str())); } PyObject* res = PyObject_Call( detail::_interpreter::get().s_python_function_stem, args, kwargs); Py_DECREF(args); Py_DECREF(kwargs); if (res) Py_DECREF(res); return res; } template< typename Numeric > bool fill(const std::vector& x, const std::vector& y, const std::map& keywords) { assert(x.size() == y.size()); detail::_interpreter::get(); // using numpy arrays PyObject* xarray = detail::get_array(x); PyObject* yarray = detail::get_array(y); // construct positional args PyObject* args = PyTuple_New(2); PyTuple_SetItem(args, 0, xarray); PyTuple_SetItem(args, 1, yarray); // construct keyword args PyObject* kwargs = PyDict_New(); for (auto it = keywords.begin(); it != keywords.end(); ++it) { PyDict_SetItemString(kwargs, it->first.c_str(), PyUnicode_FromString(it->second.c_str())); } PyObject* res = PyObject_Call(detail::_interpreter::get().s_python_function_fill, args, kwargs); Py_DECREF(args); Py_DECREF(kwargs); if (res) Py_DECREF(res); return res; } template< typename Numeric > bool fill_between(const std::vector& x, const std::vector& y1, const std::vector& y2, const std::map& keywords) { assert(x.size() == y1.size()); assert(x.size() == y2.size()); detail::_interpreter::get(); // using numpy arrays PyObject* xarray = detail::get_array(x); PyObject* y1array = detail::get_array(y1); PyObject* y2array = detail::get_array(y2); // construct positional args PyObject* args = PyTuple_New(3); PyTuple_SetItem(args, 0, xarray); PyTuple_SetItem(args, 1, y1array); PyTuple_SetItem(args, 2, y2array); // construct keyword args PyObject* kwargs = PyDict_New(); for(std::map::const_iterator it = keywords.begin(); it != keywords.end(); ++it) { PyDict_SetItemString(kwargs, it->first.c_str(), PyUnicode_FromString(it->second.c_str())); } PyObject* res = PyObject_Call(detail::_interpreter::get().s_python_function_fill_between, args, kwargs); Py_DECREF(args); Py_DECREF(kwargs); if(res) Py_DECREF(res); return res; } template bool arrow(Numeric x, Numeric y, Numeric end_x, Numeric end_y, const std::string& fc = "r", const std::string ec = "k", Numeric head_length = 0.25, Numeric head_width = 0.1625) { PyObject* obj_x = PyFloat_FromDouble(x); PyObject* obj_y = PyFloat_FromDouble(y); PyObject* obj_end_x = PyFloat_FromDouble(end_x); PyObject* obj_end_y = PyFloat_FromDouble(end_y); PyObject* kwargs = PyDict_New(); PyDict_SetItemString(kwargs, "fc", PyString_FromString(fc.c_str())); PyDict_SetItemString(kwargs, "ec", PyString_FromString(ec.c_str())); PyDict_SetItemString(kwargs, "head_width", PyFloat_FromDouble(head_width)); PyDict_SetItemString(kwargs, "head_length", PyFloat_FromDouble(head_length)); PyObject* plot_args = PyTuple_New(4); PyTuple_SetItem(plot_args, 0, obj_x); PyTuple_SetItem(plot_args, 1, obj_y); PyTuple_SetItem(plot_args, 2, obj_end_x); PyTuple_SetItem(plot_args, 3, obj_end_y); PyObject* res = PyObject_Call(detail::_interpreter::get().s_python_function_arrow, plot_args, kwargs); Py_DECREF(plot_args); Py_DECREF(kwargs); if (res) Py_DECREF(res); return res; } template< typename Numeric> bool hist(const std::vector& y, long bins=10,std::string color="b", double alpha=1.0, bool cumulative=false) { detail::_interpreter::get(); PyObject* yarray = detail::get_array(y); PyObject* kwargs = PyDict_New(); PyDict_SetItemString(kwargs, "bins", PyLong_FromLong(bins)); PyDict_SetItemString(kwargs, "color", PyString_FromString(color.c_str())); PyDict_SetItemString(kwargs, "alpha", PyFloat_FromDouble(alpha)); PyDict_SetItemString(kwargs, "cumulative", cumulative ? Py_True : Py_False); PyObject* plot_args = PyTuple_New(1); PyTuple_SetItem(plot_args, 0, yarray); PyObject* res = PyObject_Call(detail::_interpreter::get().s_python_function_hist, plot_args, kwargs); Py_DECREF(plot_args); Py_DECREF(kwargs); if(res) Py_DECREF(res); return res; } #ifndef WITHOUT_NUMPY namespace detail { inline void imshow(void *ptr, const NPY_TYPES type, const int rows, const int columns, const int colors, const std::map &keywords, PyObject** out) { assert(type == NPY_UINT8 || type == NPY_FLOAT); assert(colors == 1 || colors == 3 || colors == 4); detail::_interpreter::get(); // construct args npy_intp dims[3] = { rows, columns, colors }; PyObject *args = PyTuple_New(1); PyTuple_SetItem(args, 0, PyArray_SimpleNewFromData(colors == 1 ? 2 : 3, dims, type, ptr)); // construct keyword args PyObject* kwargs = PyDict_New(); for(std::map::const_iterator it = keywords.begin(); it != keywords.end(); ++it) { PyDict_SetItemString(kwargs, it->first.c_str(), PyUnicode_FromString(it->second.c_str())); } PyObject *res = PyObject_Call(detail::_interpreter::get().s_python_function_imshow, args, kwargs); Py_DECREF(args); Py_DECREF(kwargs); if (!res) throw std::runtime_error("Call to imshow() failed"); if (out) *out = res; else Py_DECREF(res); } } // namespace detail inline void imshow(const unsigned char *ptr, const int rows, const int columns, const int colors, const std::map &keywords = {}, PyObject** out = nullptr) { detail::imshow((void *) ptr, NPY_UINT8, rows, columns, colors, keywords, out); } inline void imshow(const float *ptr, const int rows, const int columns, const int colors, const std::map &keywords = {}, PyObject** out = nullptr) { detail::imshow((void *) ptr, NPY_FLOAT, rows, columns, colors, keywords, out); } #ifdef WITH_OPENCV void imshow(const cv::Mat &image, const std::map &keywords = {}) { // Convert underlying type of matrix, if needed cv::Mat image2; NPY_TYPES npy_type = NPY_UINT8; switch (image.type() & CV_MAT_DEPTH_MASK) { case CV_8U: image2 = image; break; case CV_32F: image2 = image; npy_type = NPY_FLOAT; break; default: image.convertTo(image2, CV_MAKETYPE(CV_8U, image.channels())); } // If color image, convert from BGR to RGB switch (image2.channels()) { case 3: cv::cvtColor(image2, image2, CV_BGR2RGB); break; case 4: cv::cvtColor(image2, image2, CV_BGRA2RGBA); } detail::imshow(image2.data, npy_type, image2.rows, image2.cols, image2.channels(), keywords); } #endif // WITH_OPENCV #endif // WITHOUT_NUMPY template bool scatter(const std::vector& x, const std::vector& y, const double s=1.0, // The marker size in points**2 const std::map & keywords = {}) { detail::_interpreter::get(); assert(x.size() == y.size()); PyObject* xarray = detail::get_array(x); PyObject* yarray = detail::get_array(y); PyObject* kwargs = PyDict_New(); PyDict_SetItemString(kwargs, "s", PyLong_FromLong(s)); for (const auto& it : keywords) { PyDict_SetItemString(kwargs, it.first.c_str(), PyString_FromString(it.second.c_str())); } PyObject* plot_args = PyTuple_New(2); PyTuple_SetItem(plot_args, 0, xarray); PyTuple_SetItem(plot_args, 1, yarray); PyObject* res = PyObject_Call(detail::_interpreter::get().s_python_function_scatter, plot_args, kwargs); Py_DECREF(plot_args); Py_DECREF(kwargs); if(res) Py_DECREF(res); return res; } template bool boxplot(const std::vector>& data, const std::vector& labels = {}, const std::map & keywords = {}) { detail::_interpreter::get(); PyObject* listlist = detail::get_listlist(data); PyObject* args = PyTuple_New(1); PyTuple_SetItem(args, 0, listlist); PyObject* kwargs = PyDict_New(); // kwargs needs the labels, if there are (the correct number of) labels if (!labels.empty() && labels.size() == data.size()) { PyDict_SetItemString(kwargs, "labels", detail::get_array(labels)); } // take care of the remaining keywords for (const auto& it : keywords) { PyDict_SetItemString(kwargs, it.first.c_str(), PyString_FromString(it.second.c_str())); } PyObject* res = PyObject_Call(detail::_interpreter::get().s_python_function_boxplot, args, kwargs); Py_DECREF(args); Py_DECREF(kwargs); if(res) Py_DECREF(res); return res; } template bool boxplot(const std::vector& data, const std::map & keywords = {}) { detail::_interpreter::get(); PyObject* vector = detail::get_array(data); PyObject* args = PyTuple_New(1); PyTuple_SetItem(args, 0, vector); PyObject* kwargs = PyDict_New(); for (const auto& it : keywords) { PyDict_SetItemString(kwargs, it.first.c_str(), PyString_FromString(it.second.c_str())); } PyObject* res = PyObject_Call(detail::_interpreter::get().s_python_function_boxplot, args, kwargs); Py_DECREF(args); Py_DECREF(kwargs); if(res) Py_DECREF(res); return res; } template bool bar(const std::vector & x, const std::vector & y, std::string ec = "black", std::string ls = "-", double lw = 1.0, const std::map & keywords = {}) { detail::_interpreter::get(); PyObject * xarray = detail::get_array(x); PyObject * yarray = detail::get_array(y); PyObject * kwargs = PyDict_New(); PyDict_SetItemString(kwargs, "ec", PyString_FromString(ec.c_str())); PyDict_SetItemString(kwargs, "ls", PyString_FromString(ls.c_str())); PyDict_SetItemString(kwargs, "lw", PyFloat_FromDouble(lw)); for (std::map::const_iterator it = keywords.begin(); it != keywords.end(); ++it) { PyDict_SetItemString( kwargs, it->first.c_str(), PyUnicode_FromString(it->second.c_str())); } PyObject * plot_args = PyTuple_New(2); PyTuple_SetItem(plot_args, 0, xarray); PyTuple_SetItem(plot_args, 1, yarray); PyObject * res = PyObject_Call( detail::_interpreter::get().s_python_function_bar, plot_args, kwargs); Py_DECREF(plot_args); Py_DECREF(kwargs); if (res) Py_DECREF(res); return res; } template bool bar(const std::vector & y, std::string ec = "black", std::string ls = "-", double lw = 1.0, const std::map & keywords = {}) { using T = typename std::remove_reference::type::value_type; detail::_interpreter::get(); std::vector x; for (std::size_t i = 0; i < y.size(); i++) { x.push_back(i); } return bar(x, y, ec, ls, lw, keywords); } inline bool subplots_adjust(const std::map& keywords = {}) { detail::_interpreter::get(); PyObject* kwargs = PyDict_New(); for (std::map::const_iterator it = keywords.begin(); it != keywords.end(); ++it) { PyDict_SetItemString(kwargs, it->first.c_str(), PyFloat_FromDouble(it->second)); } PyObject* plot_args = PyTuple_New(0); PyObject* res = PyObject_Call(detail::_interpreter::get().s_python_function_subplots_adjust, plot_args, kwargs); Py_DECREF(plot_args); Py_DECREF(kwargs); if(res) Py_DECREF(res); return res; } template< typename Numeric> bool named_hist(std::string label,const std::vector& y, long bins=10, std::string color="b", double alpha=1.0) { detail::_interpreter::get(); PyObject* yarray = detail::get_array(y); PyObject* kwargs = PyDict_New(); PyDict_SetItemString(kwargs, "label", PyString_FromString(label.c_str())); PyDict_SetItemString(kwargs, "bins", PyLong_FromLong(bins)); PyDict_SetItemString(kwargs, "color", PyString_FromString(color.c_str())); PyDict_SetItemString(kwargs, "alpha", PyFloat_FromDouble(alpha)); PyObject* plot_args = PyTuple_New(1); PyTuple_SetItem(plot_args, 0, yarray); PyObject* res = PyObject_Call(detail::_interpreter::get().s_python_function_hist, plot_args, kwargs); Py_DECREF(plot_args); Py_DECREF(kwargs); if(res) Py_DECREF(res); return res; } template bool plot(const std::vector& x, const std::vector& y, const std::string& s = "") { assert(x.size() == y.size()); detail::_interpreter::get(); PyObject* xarray = detail::get_array(x); PyObject* yarray = detail::get_array(y); PyObject* pystring = PyString_FromString(s.c_str()); PyObject* plot_args = PyTuple_New(3); PyTuple_SetItem(plot_args, 0, xarray); PyTuple_SetItem(plot_args, 1, yarray); PyTuple_SetItem(plot_args, 2, pystring); PyObject* res = PyObject_CallObject(detail::_interpreter::get().s_python_function_plot, plot_args); Py_DECREF(plot_args); if(res) Py_DECREF(res); return res; } template bool contour(const std::vector& x, const std::vector& y, const std::vector& z, const std::map& keywords = {}) { assert(x.size() == y.size() && x.size() == z.size()); PyObject* xarray = get_array(x); PyObject* yarray = get_array(y); PyObject* zarray = get_array(z); PyObject* plot_args = PyTuple_New(3); PyTuple_SetItem(plot_args, 0, xarray); PyTuple_SetItem(plot_args, 1, yarray); PyTuple_SetItem(plot_args, 2, zarray); // construct keyword args PyObject* kwargs = PyDict_New(); for (std::map::const_iterator it = keywords.begin(); it != keywords.end(); ++it) { PyDict_SetItemString(kwargs, it->first.c_str(), PyUnicode_FromString(it->second.c_str())); } PyObject* res = PyObject_Call(detail::_interpreter::get().s_python_function_contour, plot_args, kwargs); Py_DECREF(kwargs); Py_DECREF(plot_args); if (res) Py_DECREF(res); return res; } template bool quiver(const std::vector& x, const std::vector& y, const std::vector& u, const std::vector& w, const std::map& keywords = {}) { assert(x.size() == y.size() && x.size() == u.size() && u.size() == w.size()); detail::_interpreter::get(); PyObject* xarray = detail::get_array(x); PyObject* yarray = detail::get_array(y); PyObject* uarray = detail::get_array(u); PyObject* warray = detail::get_array(w); PyObject* plot_args = PyTuple_New(4); PyTuple_SetItem(plot_args, 0, xarray); PyTuple_SetItem(plot_args, 1, yarray); PyTuple_SetItem(plot_args, 2, uarray); PyTuple_SetItem(plot_args, 3, warray); // construct keyword args PyObject* kwargs = PyDict_New(); for(std::map::const_iterator it = keywords.begin(); it != keywords.end(); ++it) { PyDict_SetItemString(kwargs, it->first.c_str(), PyUnicode_FromString(it->second.c_str())); } PyObject* res = PyObject_Call( detail::_interpreter::get().s_python_function_quiver, plot_args, kwargs); Py_DECREF(kwargs); Py_DECREF(plot_args); if (res) Py_DECREF(res); return res; } template bool stem(const std::vector& x, const std::vector& y, const std::string& s = "") { assert(x.size() == y.size()); detail::_interpreter::get(); PyObject* xarray = detail::get_array(x); PyObject* yarray = detail::get_array(y); PyObject* pystring = PyString_FromString(s.c_str()); PyObject* plot_args = PyTuple_New(3); PyTuple_SetItem(plot_args, 0, xarray); PyTuple_SetItem(plot_args, 1, yarray); PyTuple_SetItem(plot_args, 2, pystring); PyObject* res = PyObject_CallObject( detail::_interpreter::get().s_python_function_stem, plot_args); Py_DECREF(plot_args); if (res) Py_DECREF(res); return res; } template bool semilogx(const std::vector& x, const std::vector& y, const std::string& s = "") { assert(x.size() == y.size()); detail::_interpreter::get(); PyObject* xarray = detail::get_array(x); PyObject* yarray = detail::get_array(y); PyObject* pystring = PyString_FromString(s.c_str()); PyObject* plot_args = PyTuple_New(3); PyTuple_SetItem(plot_args, 0, xarray); PyTuple_SetItem(plot_args, 1, yarray); PyTuple_SetItem(plot_args, 2, pystring); PyObject* res = PyObject_CallObject(detail::_interpreter::get().s_python_function_semilogx, plot_args); Py_DECREF(plot_args); if(res) Py_DECREF(res); return res; } template bool semilogy(const std::vector& x, const std::vector& y, const std::string& s = "") { assert(x.size() == y.size()); detail::_interpreter::get(); PyObject* xarray = detail::get_array(x); PyObject* yarray = detail::get_array(y); PyObject* pystring = PyString_FromString(s.c_str()); PyObject* plot_args = PyTuple_New(3); PyTuple_SetItem(plot_args, 0, xarray); PyTuple_SetItem(plot_args, 1, yarray); PyTuple_SetItem(plot_args, 2, pystring); PyObject* res = PyObject_CallObject(detail::_interpreter::get().s_python_function_semilogy, plot_args); Py_DECREF(plot_args); if(res) Py_DECREF(res); return res; } template bool loglog(const std::vector& x, const std::vector& y, const std::string& s = "") { assert(x.size() == y.size()); detail::_interpreter::get(); PyObject* xarray = detail::get_array(x); PyObject* yarray = detail::get_array(y); PyObject* pystring = PyString_FromString(s.c_str()); PyObject* plot_args = PyTuple_New(3); PyTuple_SetItem(plot_args, 0, xarray); PyTuple_SetItem(plot_args, 1, yarray); PyTuple_SetItem(plot_args, 2, pystring); PyObject* res = PyObject_CallObject(detail::_interpreter::get().s_python_function_loglog, plot_args); Py_DECREF(plot_args); if(res) Py_DECREF(res); return res; } template bool errorbar(const std::vector &x, const std::vector &y, const std::vector &yerr, const std::map &keywords = {}) { assert(x.size() == y.size()); detail::_interpreter::get(); PyObject* xarray = detail::get_array(x); PyObject* yarray = detail::get_array(y); PyObject* yerrarray = detail::get_array(yerr); // construct keyword args PyObject* kwargs = PyDict_New(); for(std::map::const_iterator it = keywords.begin(); it != keywords.end(); ++it) { PyDict_SetItemString(kwargs, it->first.c_str(), PyString_FromString(it->second.c_str())); } PyDict_SetItemString(kwargs, "yerr", yerrarray); PyObject *plot_args = PyTuple_New(2); PyTuple_SetItem(plot_args, 0, xarray); PyTuple_SetItem(plot_args, 1, yarray); PyObject *res = PyObject_Call(detail::_interpreter::get().s_python_function_errorbar, plot_args, kwargs); Py_DECREF(kwargs); Py_DECREF(plot_args); if (res) Py_DECREF(res); else throw std::runtime_error("Call to errorbar() failed."); return res; } template bool named_plot(const std::string& name, const std::vector& y, const std::string& format = "") { detail::_interpreter::get(); PyObject* kwargs = PyDict_New(); PyDict_SetItemString(kwargs, "label", PyString_FromString(name.c_str())); PyObject* yarray = detail::get_array(y); PyObject* pystring = PyString_FromString(format.c_str()); PyObject* plot_args = PyTuple_New(2); PyTuple_SetItem(plot_args, 0, yarray); PyTuple_SetItem(plot_args, 1, pystring); PyObject* res = PyObject_Call(detail::_interpreter::get().s_python_function_plot, plot_args, kwargs); Py_DECREF(kwargs); Py_DECREF(plot_args); if (res) Py_DECREF(res); return res; } template bool named_plot(const std::string& name, const std::vector& x, const std::vector& y, const std::string& format = "") { detail::_interpreter::get(); PyObject* kwargs = PyDict_New(); PyDict_SetItemString(kwargs, "label", PyString_FromString(name.c_str())); PyObject* xarray = detail::get_array(x); PyObject* yarray = detail::get_array(y); PyObject* pystring = PyString_FromString(format.c_str()); PyObject* plot_args = PyTuple_New(3); PyTuple_SetItem(plot_args, 0, xarray); PyTuple_SetItem(plot_args, 1, yarray); PyTuple_SetItem(plot_args, 2, pystring); PyObject* res = PyObject_Call(detail::_interpreter::get().s_python_function_plot, plot_args, kwargs); Py_DECREF(kwargs); Py_DECREF(plot_args); if (res) Py_DECREF(res); return res; } template bool named_semilogx(const std::string& name, const std::vector& x, const std::vector& y, const std::string& format = "") { detail::_interpreter::get(); PyObject* kwargs = PyDict_New(); PyDict_SetItemString(kwargs, "label", PyString_FromString(name.c_str())); PyObject* xarray = detail::get_array(x); PyObject* yarray = detail::get_array(y); PyObject* pystring = PyString_FromString(format.c_str()); PyObject* plot_args = PyTuple_New(3); PyTuple_SetItem(plot_args, 0, xarray); PyTuple_SetItem(plot_args, 1, yarray); PyTuple_SetItem(plot_args, 2, pystring); PyObject* res = PyObject_Call(detail::_interpreter::get().s_python_function_semilogx, plot_args, kwargs); Py_DECREF(kwargs); Py_DECREF(plot_args); if (res) Py_DECREF(res); return res; } template bool named_semilogy(const std::string& name, const std::vector& x, const std::vector& y, const std::string& format = "") { detail::_interpreter::get(); PyObject* kwargs = PyDict_New(); PyDict_SetItemString(kwargs, "label", PyString_FromString(name.c_str())); PyObject* xarray = detail::get_array(x); PyObject* yarray = detail::get_array(y); PyObject* pystring = PyString_FromString(format.c_str()); PyObject* plot_args = PyTuple_New(3); PyTuple_SetItem(plot_args, 0, xarray); PyTuple_SetItem(plot_args, 1, yarray); PyTuple_SetItem(plot_args, 2, pystring); PyObject* res = PyObject_Call(detail::_interpreter::get().s_python_function_semilogy, plot_args, kwargs); Py_DECREF(kwargs); Py_DECREF(plot_args); if (res) Py_DECREF(res); return res; } template bool named_loglog(const std::string& name, const std::vector& x, const std::vector& y, const std::string& format = "") { detail::_interpreter::get(); PyObject* kwargs = PyDict_New(); PyDict_SetItemString(kwargs, "label", PyString_FromString(name.c_str())); PyObject* xarray = detail::get_array(x); PyObject* yarray = detail::get_array(y); PyObject* pystring = PyString_FromString(format.c_str()); PyObject* plot_args = PyTuple_New(3); PyTuple_SetItem(plot_args, 0, xarray); PyTuple_SetItem(plot_args, 1, yarray); PyTuple_SetItem(plot_args, 2, pystring); PyObject* res = PyObject_Call(detail::_interpreter::get().s_python_function_loglog, plot_args, kwargs); Py_DECREF(kwargs); Py_DECREF(plot_args); if (res) Py_DECREF(res); return res; } template bool plot(const std::vector& y, const std::string& format = "") { std::vector x(y.size()); for(size_t i=0; i bool plot(const std::vector& y, const std::map& keywords) { std::vector x(y.size()); for(size_t i=0; i bool stem(const std::vector& y, const std::string& format = "") { std::vector x(y.size()); for (size_t i = 0; i < x.size(); ++i) x.at(i) = i; return stem(x, y, format); } template void text(Numeric x, Numeric y, const std::string& s = "") { detail::_interpreter::get(); PyObject* args = PyTuple_New(3); PyTuple_SetItem(args, 0, PyFloat_FromDouble(x)); PyTuple_SetItem(args, 1, PyFloat_FromDouble(y)); PyTuple_SetItem(args, 2, PyString_FromString(s.c_str())); PyObject* res = PyObject_CallObject(detail::_interpreter::get().s_python_function_text, args); if(!res) throw std::runtime_error("Call to text() failed."); Py_DECREF(args); Py_DECREF(res); } inline void colorbar(PyObject* mappable = NULL, const std::map& keywords = {}) { if (mappable == NULL) throw std::runtime_error("Must call colorbar with PyObject* returned from an image, contour, surface, etc."); detail::_interpreter::get(); PyObject* args = PyTuple_New(1); PyTuple_SetItem(args, 0, mappable); PyObject* kwargs = PyDict_New(); for(std::map::const_iterator it = keywords.begin(); it != keywords.end(); ++it) { PyDict_SetItemString(kwargs, it->first.c_str(), PyFloat_FromDouble(it->second)); } PyObject* res = PyObject_Call(detail::_interpreter::get().s_python_function_colorbar, args, kwargs); if(!res) throw std::runtime_error("Call to colorbar() failed."); Py_DECREF(args); Py_DECREF(kwargs); Py_DECREF(res); } inline long figure(long number = -1) { detail::_interpreter::get(); PyObject *res; if (number == -1) res = PyObject_CallObject(detail::_interpreter::get().s_python_function_figure, detail::_interpreter::get().s_python_empty_tuple); else { assert(number > 0); // Make sure interpreter is initialised detail::_interpreter::get(); PyObject *args = PyTuple_New(1); PyTuple_SetItem(args, 0, PyLong_FromLong(number)); res = PyObject_CallObject(detail::_interpreter::get().s_python_function_figure, args); Py_DECREF(args); } if(!res) throw std::runtime_error("Call to figure() failed."); PyObject* num = PyObject_GetAttrString(res, "number"); if (!num) throw std::runtime_error("Could not get number attribute of figure object"); const long figureNumber = PyLong_AsLong(num); Py_DECREF(num); Py_DECREF(res); return figureNumber; } inline bool fignum_exists(long number) { detail::_interpreter::get(); PyObject *args = PyTuple_New(1); PyTuple_SetItem(args, 0, PyLong_FromLong(number)); PyObject *res = PyObject_CallObject(detail::_interpreter::get().s_python_function_fignum_exists, args); if(!res) throw std::runtime_error("Call to fignum_exists() failed."); bool ret = PyObject_IsTrue(res); Py_DECREF(res); Py_DECREF(args); return ret; } inline void figure_size(size_t w, size_t h) { detail::_interpreter::get(); const size_t dpi = 100; PyObject* size = PyTuple_New(2); PyTuple_SetItem(size, 0, PyFloat_FromDouble((double)w / dpi)); PyTuple_SetItem(size, 1, PyFloat_FromDouble((double)h / dpi)); PyObject* kwargs = PyDict_New(); PyDict_SetItemString(kwargs, "figsize", size); PyDict_SetItemString(kwargs, "dpi", PyLong_FromSize_t(dpi)); PyObject* res = PyObject_Call(detail::_interpreter::get().s_python_function_figure, detail::_interpreter::get().s_python_empty_tuple, kwargs); Py_DECREF(kwargs); if(!res) throw std::runtime_error("Call to figure_size() failed."); Py_DECREF(res); } inline void legend() { detail::_interpreter::get(); PyObject* res = PyObject_CallObject(detail::_interpreter::get().s_python_function_legend, detail::_interpreter::get().s_python_empty_tuple); if(!res) throw std::runtime_error("Call to legend() failed."); Py_DECREF(res); } inline void legend(const std::map& keywords) { detail::_interpreter::get(); // construct keyword args PyObject* kwargs = PyDict_New(); for(std::map::const_iterator it = keywords.begin(); it != keywords.end(); ++it) { PyDict_SetItemString(kwargs, it->first.c_str(), PyString_FromString(it->second.c_str())); } PyObject* res = PyObject_Call(detail::_interpreter::get().s_python_function_legend, detail::_interpreter::get().s_python_empty_tuple, kwargs); if(!res) throw std::runtime_error("Call to legend() failed."); Py_DECREF(kwargs); Py_DECREF(res); } template void ylim(Numeric left, Numeric right) { detail::_interpreter::get(); PyObject* list = PyList_New(2); PyList_SetItem(list, 0, PyFloat_FromDouble(left)); PyList_SetItem(list, 1, PyFloat_FromDouble(right)); PyObject* args = PyTuple_New(1); PyTuple_SetItem(args, 0, list); PyObject* res = PyObject_CallObject(detail::_interpreter::get().s_python_function_ylim, args); if(!res) throw std::runtime_error("Call to ylim() failed."); Py_DECREF(args); Py_DECREF(res); } template void xlim(Numeric left, Numeric right) { detail::_interpreter::get(); PyObject* list = PyList_New(2); PyList_SetItem(list, 0, PyFloat_FromDouble(left)); PyList_SetItem(list, 1, PyFloat_FromDouble(right)); PyObject* args = PyTuple_New(1); PyTuple_SetItem(args, 0, list); PyObject* res = PyObject_CallObject(detail::_interpreter::get().s_python_function_xlim, args); if(!res) throw std::runtime_error("Call to xlim() failed."); Py_DECREF(args); Py_DECREF(res); } inline double* xlim() { detail::_interpreter::get(); PyObject* args = PyTuple_New(0); PyObject* res = PyObject_CallObject(detail::_interpreter::get().s_python_function_xlim, args); PyObject* left = PyTuple_GetItem(res,0); PyObject* right = PyTuple_GetItem(res,1); double* arr = new double[2]; arr[0] = PyFloat_AsDouble(left); arr[1] = PyFloat_AsDouble(right); if(!res) throw std::runtime_error("Call to xlim() failed."); Py_DECREF(res); return arr; } inline double* ylim() { detail::_interpreter::get(); PyObject* args = PyTuple_New(0); PyObject* res = PyObject_CallObject(detail::_interpreter::get().s_python_function_ylim, args); PyObject* left = PyTuple_GetItem(res,0); PyObject* right = PyTuple_GetItem(res,1); double* arr = new double[2]; arr[0] = PyFloat_AsDouble(left); arr[1] = PyFloat_AsDouble(right); if(!res) throw std::runtime_error("Call to ylim() failed."); Py_DECREF(res); return arr; } template inline void xticks(const std::vector &ticks, const std::vector &labels = {}, const std::map& keywords = {}) { assert(labels.size() == 0 || ticks.size() == labels.size()); detail::_interpreter::get(); // using numpy array PyObject* ticksarray = detail::get_array(ticks); PyObject* args; if(labels.size() == 0) { // construct positional args args = PyTuple_New(1); PyTuple_SetItem(args, 0, ticksarray); } else { // make tuple of tick labels PyObject* labelstuple = PyTuple_New(labels.size()); for (size_t i = 0; i < labels.size(); i++) PyTuple_SetItem(labelstuple, i, PyUnicode_FromString(labels[i].c_str())); // construct positional args args = PyTuple_New(2); PyTuple_SetItem(args, 0, ticksarray); PyTuple_SetItem(args, 1, labelstuple); } // construct keyword args PyObject* kwargs = PyDict_New(); for(std::map::const_iterator it = keywords.begin(); it != keywords.end(); ++it) { PyDict_SetItemString(kwargs, it->first.c_str(), PyString_FromString(it->second.c_str())); } PyObject* res = PyObject_Call(detail::_interpreter::get().s_python_function_xticks, args, kwargs); Py_DECREF(args); Py_DECREF(kwargs); if(!res) throw std::runtime_error("Call to xticks() failed"); Py_DECREF(res); } template inline void xticks(const std::vector &ticks, const std::map& keywords) { xticks(ticks, {}, keywords); } template inline void yticks(const std::vector &ticks, const std::vector &labels = {}, const std::map& keywords = {}) { assert(labels.size() == 0 || ticks.size() == labels.size()); detail::_interpreter::get(); // using numpy array PyObject* ticksarray = detail::get_array(ticks); PyObject* args; if(labels.size() == 0) { // construct positional args args = PyTuple_New(1); PyTuple_SetItem(args, 0, ticksarray); } else { // make tuple of tick labels PyObject* labelstuple = PyTuple_New(labels.size()); for (size_t i = 0; i < labels.size(); i++) PyTuple_SetItem(labelstuple, i, PyUnicode_FromString(labels[i].c_str())); // construct positional args args = PyTuple_New(2); PyTuple_SetItem(args, 0, ticksarray); PyTuple_SetItem(args, 1, labelstuple); } // construct keyword args PyObject* kwargs = PyDict_New(); for(std::map::const_iterator it = keywords.begin(); it != keywords.end(); ++it) { PyDict_SetItemString(kwargs, it->first.c_str(), PyString_FromString(it->second.c_str())); } PyObject* res = PyObject_Call(detail::_interpreter::get().s_python_function_yticks, args, kwargs); Py_DECREF(args); Py_DECREF(kwargs); if(!res) throw std::runtime_error("Call to yticks() failed"); Py_DECREF(res); } template inline void yticks(const std::vector &ticks, const std::map& keywords) { yticks(ticks, {}, keywords); } template inline void margins(Numeric margin) { // construct positional args PyObject* args = PyTuple_New(1); PyTuple_SetItem(args, 0, PyFloat_FromDouble(margin)); PyObject* res = PyObject_CallObject(detail::_interpreter::get().s_python_function_margins, args); if (!res) throw std::runtime_error("Call to margins() failed."); Py_DECREF(args); Py_DECREF(res); } template inline void margins(Numeric margin_x, Numeric margin_y) { // construct positional args PyObject* args = PyTuple_New(2); PyTuple_SetItem(args, 0, PyFloat_FromDouble(margin_x)); PyTuple_SetItem(args, 1, PyFloat_FromDouble(margin_y)); PyObject* res = PyObject_CallObject(detail::_interpreter::get().s_python_function_margins, args); if (!res) throw std::runtime_error("Call to margins() failed."); Py_DECREF(args); Py_DECREF(res); } inline void tick_params(const std::map& keywords, const std::string axis = "both") { detail::_interpreter::get(); // construct positional args PyObject* args; args = PyTuple_New(1); PyTuple_SetItem(args, 0, PyString_FromString(axis.c_str())); // construct keyword args PyObject* kwargs = PyDict_New(); for (std::map::const_iterator it = keywords.begin(); it != keywords.end(); ++it) { PyDict_SetItemString(kwargs, it->first.c_str(), PyString_FromString(it->second.c_str())); } PyObject* res = PyObject_Call(detail::_interpreter::get().s_python_function_tick_params, args, kwargs); Py_DECREF(args); Py_DECREF(kwargs); if (!res) throw std::runtime_error("Call to tick_params() failed"); Py_DECREF(res); } inline void subplot(long nrows, long ncols, long plot_number) { detail::_interpreter::get(); // construct positional args PyObject* args = PyTuple_New(3); PyTuple_SetItem(args, 0, PyFloat_FromDouble(nrows)); PyTuple_SetItem(args, 1, PyFloat_FromDouble(ncols)); PyTuple_SetItem(args, 2, PyFloat_FromDouble(plot_number)); PyObject* res = PyObject_CallObject(detail::_interpreter::get().s_python_function_subplot, args); if(!res) throw std::runtime_error("Call to subplot() failed."); Py_DECREF(args); Py_DECREF(res); } inline void subplot2grid(long nrows, long ncols, long rowid=0, long colid=0, long rowspan=1, long colspan=1) { detail::_interpreter::get(); PyObject* shape = PyTuple_New(2); PyTuple_SetItem(shape, 0, PyLong_FromLong(nrows)); PyTuple_SetItem(shape, 1, PyLong_FromLong(ncols)); PyObject* loc = PyTuple_New(2); PyTuple_SetItem(loc, 0, PyLong_FromLong(rowid)); PyTuple_SetItem(loc, 1, PyLong_FromLong(colid)); PyObject* args = PyTuple_New(4); PyTuple_SetItem(args, 0, shape); PyTuple_SetItem(args, 1, loc); PyTuple_SetItem(args, 2, PyLong_FromLong(rowspan)); PyTuple_SetItem(args, 3, PyLong_FromLong(colspan)); PyObject* res = PyObject_CallObject(detail::_interpreter::get().s_python_function_subplot2grid, args); if(!res) throw std::runtime_error("Call to subplot2grid() failed."); Py_DECREF(shape); Py_DECREF(loc); Py_DECREF(args); Py_DECREF(res); } inline void title(const std::string &titlestr, const std::map &keywords = {}) { detail::_interpreter::get(); PyObject* pytitlestr = PyString_FromString(titlestr.c_str()); PyObject* args = PyTuple_New(1); PyTuple_SetItem(args, 0, pytitlestr); PyObject* kwargs = PyDict_New(); for (auto it = keywords.begin(); it != keywords.end(); ++it) { PyDict_SetItemString(kwargs, it->first.c_str(), PyUnicode_FromString(it->second.c_str())); } PyObject* res = PyObject_Call(detail::_interpreter::get().s_python_function_title, args, kwargs); if(!res) throw std::runtime_error("Call to title() failed."); Py_DECREF(args); Py_DECREF(kwargs); Py_DECREF(res); } inline void suptitle(const std::string &suptitlestr, const std::map &keywords = {}) { detail::_interpreter::get(); PyObject* pysuptitlestr = PyString_FromString(suptitlestr.c_str()); PyObject* args = PyTuple_New(1); PyTuple_SetItem(args, 0, pysuptitlestr); PyObject* kwargs = PyDict_New(); for (auto it = keywords.begin(); it != keywords.end(); ++it) { PyDict_SetItemString(kwargs, it->first.c_str(), PyUnicode_FromString(it->second.c_str())); } PyObject* res = PyObject_Call(detail::_interpreter::get().s_python_function_suptitle, args, kwargs); if(!res) throw std::runtime_error("Call to suptitle() failed."); Py_DECREF(args); Py_DECREF(kwargs); Py_DECREF(res); } inline void axis(const std::string &axisstr) { detail::_interpreter::get(); PyObject* str = PyString_FromString(axisstr.c_str()); PyObject* args = PyTuple_New(1); PyTuple_SetItem(args, 0, str); PyObject* res = PyObject_CallObject(detail::_interpreter::get().s_python_function_axis, args); if(!res) throw std::runtime_error("Call to title() failed."); Py_DECREF(args); Py_DECREF(res); } inline void axvline(double x, double ymin = 0., double ymax = 1., const std::map& keywords = std::map()) { detail::_interpreter::get(); // construct positional args PyObject* args = PyTuple_New(3); PyTuple_SetItem(args, 0, PyFloat_FromDouble(x)); PyTuple_SetItem(args, 1, PyFloat_FromDouble(ymin)); PyTuple_SetItem(args, 2, PyFloat_FromDouble(ymax)); // construct keyword args PyObject* kwargs = PyDict_New(); for(std::map::const_iterator it = keywords.begin(); it != keywords.end(); ++it) { PyDict_SetItemString(kwargs, it->first.c_str(), PyString_FromString(it->second.c_str())); } PyObject* res = PyObject_Call(detail::_interpreter::get().s_python_function_axvline, args, kwargs); Py_DECREF(args); Py_DECREF(kwargs); if(res) Py_DECREF(res); } inline void axvspan(double xmin, double xmax, double ymin = 0., double ymax = 1., const std::map& keywords = std::map()) { // construct positional args PyObject* args = PyTuple_New(4); PyTuple_SetItem(args, 0, PyFloat_FromDouble(xmin)); PyTuple_SetItem(args, 1, PyFloat_FromDouble(xmax)); PyTuple_SetItem(args, 2, PyFloat_FromDouble(ymin)); PyTuple_SetItem(args, 3, PyFloat_FromDouble(ymax)); // construct keyword args PyObject* kwargs = PyDict_New(); for(std::map::const_iterator it = keywords.begin(); it != keywords.end(); ++it) { if (it->first == "linewidth" || it->first == "alpha") PyDict_SetItemString(kwargs, it->first.c_str(), PyFloat_FromDouble(std::stod(it->second))); else PyDict_SetItemString(kwargs, it->first.c_str(), PyString_FromString(it->second.c_str())); } PyObject* res = PyObject_Call(detail::_interpreter::get().s_python_function_axvspan, args, kwargs); Py_DECREF(args); Py_DECREF(kwargs); if(res) Py_DECREF(res); } inline void xlabel(const std::string &str, const std::map &keywords = {}) { detail::_interpreter::get(); PyObject* pystr = PyString_FromString(str.c_str()); PyObject* args = PyTuple_New(1); PyTuple_SetItem(args, 0, pystr); PyObject* kwargs = PyDict_New(); for (auto it = keywords.begin(); it != keywords.end(); ++it) { PyDict_SetItemString(kwargs, it->first.c_str(), PyUnicode_FromString(it->second.c_str())); } PyObject* res = PyObject_Call(detail::_interpreter::get().s_python_function_xlabel, args, kwargs); if(!res) throw std::runtime_error("Call to xlabel() failed."); Py_DECREF(args); Py_DECREF(kwargs); Py_DECREF(res); } inline void ylabel(const std::string &str, const std::map& keywords = {}) { detail::_interpreter::get(); PyObject* pystr = PyString_FromString(str.c_str()); PyObject* args = PyTuple_New(1); PyTuple_SetItem(args, 0, pystr); PyObject* kwargs = PyDict_New(); for (auto it = keywords.begin(); it != keywords.end(); ++it) { PyDict_SetItemString(kwargs, it->first.c_str(), PyUnicode_FromString(it->second.c_str())); } PyObject* res = PyObject_Call(detail::_interpreter::get().s_python_function_ylabel, args, kwargs); if(!res) throw std::runtime_error("Call to ylabel() failed."); Py_DECREF(args); Py_DECREF(kwargs); Py_DECREF(res); } inline void set_zlabel(const std::string &str, const std::map& keywords = {}) { detail::_interpreter::get(); // Same as with plot_surface: We lazily load the modules here the first time // this function is called because I'm not sure that we can assume "matplotlib // installed" implies "mpl_toolkits installed" on all platforms, and we don't // want to require it for people who don't need 3d plots. static PyObject *mpl_toolkitsmod = nullptr, *axis3dmod = nullptr; if (!mpl_toolkitsmod) { PyObject* mpl_toolkits = PyString_FromString("mpl_toolkits"); PyObject* axis3d = PyString_FromString("mpl_toolkits.mplot3d"); if (!mpl_toolkits || !axis3d) { throw std::runtime_error("couldnt create string"); } mpl_toolkitsmod = PyImport_Import(mpl_toolkits); Py_DECREF(mpl_toolkits); if (!mpl_toolkitsmod) { throw std::runtime_error("Error loading module mpl_toolkits!"); } axis3dmod = PyImport_Import(axis3d); Py_DECREF(axis3d); if (!axis3dmod) { throw std::runtime_error("Error loading module mpl_toolkits.mplot3d!"); } } PyObject* pystr = PyString_FromString(str.c_str()); PyObject* args = PyTuple_New(1); PyTuple_SetItem(args, 0, pystr); PyObject* kwargs = PyDict_New(); for (auto it = keywords.begin(); it != keywords.end(); ++it) { PyDict_SetItemString(kwargs, it->first.c_str(), PyUnicode_FromString(it->second.c_str())); } PyObject *ax = PyObject_CallObject(detail::_interpreter::get().s_python_function_gca, detail::_interpreter::get().s_python_empty_tuple); if (!ax) throw std::runtime_error("Call to gca() failed."); Py_INCREF(ax); PyObject *zlabel = PyObject_GetAttrString(ax, "set_zlabel"); if (!zlabel) throw std::runtime_error("Attribute set_zlabel not found."); Py_INCREF(zlabel); PyObject *res = PyObject_Call(zlabel, args, kwargs); if (!res) throw std::runtime_error("Call to set_zlabel() failed."); Py_DECREF(zlabel); Py_DECREF(ax); Py_DECREF(args); Py_DECREF(kwargs); if (res) Py_DECREF(res); } inline void grid(bool flag) { detail::_interpreter::get(); PyObject* pyflag = flag ? Py_True : Py_False; Py_INCREF(pyflag); PyObject* args = PyTuple_New(1); PyTuple_SetItem(args, 0, pyflag); PyObject* res = PyObject_CallObject(detail::_interpreter::get().s_python_function_grid, args); if(!res) throw std::runtime_error("Call to grid() failed."); Py_DECREF(args); Py_DECREF(res); } inline void show(const bool block = true) { detail::_interpreter::get(); PyObject* res; if(block) { res = PyObject_CallObject( detail::_interpreter::get().s_python_function_show, detail::_interpreter::get().s_python_empty_tuple); } else { PyObject *kwargs = PyDict_New(); PyDict_SetItemString(kwargs, "block", Py_False); res = PyObject_Call( detail::_interpreter::get().s_python_function_show, detail::_interpreter::get().s_python_empty_tuple, kwargs); Py_DECREF(kwargs); } if (!res) throw std::runtime_error("Call to show() failed."); Py_DECREF(res); } inline void close() { detail::_interpreter::get(); PyObject* res = PyObject_CallObject( detail::_interpreter::get().s_python_function_close, detail::_interpreter::get().s_python_empty_tuple); if (!res) throw std::runtime_error("Call to close() failed."); Py_DECREF(res); } inline void xkcd() { detail::_interpreter::get(); PyObject* res; PyObject *kwargs = PyDict_New(); res = PyObject_Call(detail::_interpreter::get().s_python_function_xkcd, detail::_interpreter::get().s_python_empty_tuple, kwargs); Py_DECREF(kwargs); if (!res) throw std::runtime_error("Call to show() failed."); Py_DECREF(res); } inline void draw() { detail::_interpreter::get(); PyObject* res = PyObject_CallObject( detail::_interpreter::get().s_python_function_draw, detail::_interpreter::get().s_python_empty_tuple); if (!res) throw std::runtime_error("Call to draw() failed."); Py_DECREF(res); } template inline void pause(Numeric interval) { detail::_interpreter::get(); PyObject* args = PyTuple_New(1); PyTuple_SetItem(args, 0, PyFloat_FromDouble(interval)); PyObject* res = PyObject_CallObject(detail::_interpreter::get().s_python_function_pause, args); if(!res) throw std::runtime_error("Call to pause() failed."); Py_DECREF(args); Py_DECREF(res); } inline void save(const std::string& filename) { detail::_interpreter::get(); PyObject* pyfilename = PyString_FromString(filename.c_str()); PyObject* args = PyTuple_New(1); PyTuple_SetItem(args, 0, pyfilename); std::cout<<"args:"<> ginput(const int numClicks = 1, const std::map& keywords = {}) { detail::_interpreter::get(); PyObject *args = PyTuple_New(1); PyTuple_SetItem(args, 0, PyLong_FromLong(numClicks)); // construct keyword args PyObject* kwargs = PyDict_New(); for(std::map::const_iterator it = keywords.begin(); it != keywords.end(); ++it) { PyDict_SetItemString(kwargs, it->first.c_str(), PyUnicode_FromString(it->second.c_str())); } PyObject* res = PyObject_Call( detail::_interpreter::get().s_python_function_ginput, args, kwargs); Py_DECREF(kwargs); Py_DECREF(args); if (!res) throw std::runtime_error("Call to ginput() failed."); const size_t len = PyList_Size(res); std::vector> out; out.reserve(len); for (size_t i = 0; i < len; i++) { PyObject *current = PyList_GetItem(res, i); std::array position; position[0] = PyFloat_AsDouble(PyTuple_GetItem(current, 0)); position[1] = PyFloat_AsDouble(PyTuple_GetItem(current, 1)); out.push_back(position); } Py_DECREF(res); return out; } // Actually, is there any reason not to call this automatically for every plot? inline void tight_layout() { detail::_interpreter::get(); PyObject *res = PyObject_CallObject( detail::_interpreter::get().s_python_function_tight_layout, detail::_interpreter::get().s_python_empty_tuple); if (!res) throw std::runtime_error("Call to tight_layout() failed."); Py_DECREF(res); } // Support for variadic plot() and initializer lists: namespace detail { template using is_function = typename std::is_function>>::type; template struct is_callable_impl; template struct is_callable_impl { typedef is_function type; }; // a non-object is callable iff it is a function template struct is_callable_impl { struct Fallback { void operator()(); }; struct Derived : T, Fallback { }; template struct Check; template static std::true_type test( ... ); // use a variadic function to make sure (1) it accepts everything and (2) its always the worst match template static std::false_type test( Check* ); public: typedef decltype(test(nullptr)) type; typedef decltype(&Fallback::operator()) dtype; static constexpr bool value = type::value; }; // an object is callable iff it defines operator() template struct is_callable { // dispatch to is_callable_impl or is_callable_impl depending on whether T is of class type or not typedef typename is_callable_impl::value, T>::type type; }; template struct plot_impl { }; template<> struct plot_impl { template bool operator()(const IterableX& x, const IterableY& y, const std::string& format) { // 2-phase lookup for distance, begin, end using std::distance; using std::begin; using std::end; auto xs = distance(begin(x), end(x)); auto ys = distance(begin(y), end(y)); assert(xs == ys && "x and y data must have the same number of elements!"); PyObject* xlist = PyList_New(xs); PyObject* ylist = PyList_New(ys); PyObject* pystring = PyString_FromString(format.c_str()); auto itx = begin(x), ity = begin(y); for(size_t i = 0; i < xs; ++i) { PyList_SetItem(xlist, i, PyFloat_FromDouble(*itx++)); PyList_SetItem(ylist, i, PyFloat_FromDouble(*ity++)); } PyObject* plot_args = PyTuple_New(3); PyTuple_SetItem(plot_args, 0, xlist); PyTuple_SetItem(plot_args, 1, ylist); PyTuple_SetItem(plot_args, 2, pystring); PyObject* res = PyObject_CallObject(detail::_interpreter::get().s_python_function_plot, plot_args); Py_DECREF(plot_args); if(res) Py_DECREF(res); return res; } }; template<> struct plot_impl { template bool operator()(const Iterable& ticks, const Callable& f, const std::string& format) { if(begin(ticks) == end(ticks)) return true; // We could use additional meta-programming to deduce the correct element type of y, // but all values have to be convertible to double anyways std::vector y; for(auto x : ticks) y.push_back(f(x)); return plot_impl()(ticks,y,format); } }; } // end namespace detail // recursion stop for the above template bool plot() { return true; } template bool plot(const A& a, const B& b, const std::string& format, Args... args) { return detail::plot_impl::type>()(a,b,format) && plot(args...); } /* * This group of plot() functions is needed to support initializer lists, i.e. calling * plot( {1,2,3,4} ) */ inline bool plot(const std::vector& x, const std::vector& y, const std::string& format = "") { return plot(x,y,format); } inline bool plot(const std::vector& y, const std::string& format = "") { return plot(y,format); } inline bool plot(const std::vector& x, const std::vector& y, const std::map& keywords) { return plot(x,y,keywords); } /* * This class allows dynamic plots, ie changing the plotted data without clearing and re-plotting */ class Plot { public: // default initialization with plot label, some data and format template Plot(const std::string& name, const std::vector& x, const std::vector& y, const std::string& format = "") { detail::_interpreter::get(); assert(x.size() == y.size()); PyObject* kwargs = PyDict_New(); if(name != "") PyDict_SetItemString(kwargs, "label", PyString_FromString(name.c_str())); PyObject* xarray = detail::get_array(x); PyObject* yarray = detail::get_array(y); PyObject* pystring = PyString_FromString(format.c_str()); PyObject* plot_args = PyTuple_New(3); PyTuple_SetItem(plot_args, 0, xarray); PyTuple_SetItem(plot_args, 1, yarray); PyTuple_SetItem(plot_args, 2, pystring); PyObject* res = PyObject_Call(detail::_interpreter::get().s_python_function_plot, plot_args, kwargs); Py_DECREF(kwargs); Py_DECREF(plot_args); if(res) { line= PyList_GetItem(res, 0); if(line) set_data_fct = PyObject_GetAttrString(line,"set_data"); else Py_DECREF(line); Py_DECREF(res); } } // shorter initialization with name or format only // basically calls line, = plot([], []) Plot(const std::string& name = "", const std::string& format = "") : Plot(name, std::vector(), std::vector(), format) {} template bool update(const std::vector& x, const std::vector& y) { assert(x.size() == y.size()); if(set_data_fct) { PyObject* xarray = detail::get_array(x); PyObject* yarray = detail::get_array(y); PyObject* plot_args = PyTuple_New(2); PyTuple_SetItem(plot_args, 0, xarray); PyTuple_SetItem(plot_args, 1, yarray); PyObject* res = PyObject_CallObject(set_data_fct, plot_args); if (res) Py_DECREF(res); return res; } return false; } // clears the plot but keep it available bool clear() { return update(std::vector(), std::vector()); } // definitely remove this line void remove() { if(line) { auto remove_fct = PyObject_GetAttrString(line,"remove"); PyObject* args = PyTuple_New(0); PyObject* res = PyObject_CallObject(remove_fct, args); if (res) Py_DECREF(res); } decref(); } ~Plot() { decref(); } private: void decref() { if(line) Py_DECREF(line); if(set_data_fct) Py_DECREF(set_data_fct); } PyObject* line = nullptr; PyObject* set_data_fct = nullptr; }; } // end namespace matplotlibcpp ================================================ FILE: include/point_type.h ================================================ // // Created by hk on 12/8/22. // #pragma once #define PCL_NO_PRECOMPILE #include #include #include #ifndef LIO_POINT_TYPE_H #define LIO_POINT_TYPE_H struct ikdTree_PointType { PCL_ADD_POINT4D PCL_ADD_NORMAL4D; union { struct { float intensity; float curvature; float s_xx; float s_xy; }; float data_c[4]; }; // Eigen::Matrix3f S; // summation p * p^t // Eigen::Vector3f mean; // union { struct { float s_xz; float s_yy; float s_yz; float s_zz; }; float data_s[4]; }; union { struct { int num_hit; float mean_x; float mean_y; float mean_z; }; float data_num[4]; }; union { struct { float cov_xx; float cov_yy; float cov_zz; float cov_xy; }; float data_cov_1[4]; }; union { struct { float cov_xz; float cov_yz; float n_cov_xx; float n_cov_xy; }; float data_cov_2[4]; }; union { struct { float n_cov_xz; float n_cov_yz; float n_cov_zz; float n_cov_yy; }; float data_cov_3[4]; }; EIGEN_MAKE_ALIGNED_OPERATOR_NEW Eigen::Matrix3d getPointCovMatrix() const { Eigen::Matrix3d m; m(0, 0) = cov_xx; m(0, 1) = cov_xy; m(0, 2) = cov_xz; m(1, 0) = cov_xy; m(1, 1) = cov_yy; m(1, 2) = cov_yz; m(2, 0) = cov_xz; m(2, 1) = cov_yz; m(2, 2) = cov_zz; return m; } void getPointCovMatrix(Eigen::Matrix3d & m) const { m(0, 0) = cov_xx; m(0, 1) = cov_xy; m(0, 2) = cov_xz; m(1, 0) = cov_xy; m(1, 1) = cov_yy; m(1, 2) = cov_yz; m(2, 0) = cov_xz; m(2, 1) = cov_yz; m(2, 2) = cov_zz; } void getNormalCovMatrix(Eigen::Matrix3d & m) const { m(0, 0) = n_cov_xx; m(0, 1) = n_cov_xy; m(0, 2) = n_cov_xz; m(1, 0) = n_cov_xy; m(1, 1) = n_cov_yy; m(1, 2) = n_cov_yz; m(2, 0) = n_cov_xz; m(2, 1) = n_cov_yz; m(2, 2) = n_cov_zz; } void recordPointCovFromMatrix(const Eigen::Matrix3f & m) { cov_xx = m(0, 0); cov_xy = m(0, 1); cov_xz = m(0, 2); cov_yy = m(1, 1); cov_yz = m(1, 2); cov_zz = m(2, 2); } void recordNormalCovFromMatrix(const Eigen::Matrix3f & m) { n_cov_xx = m(0, 0); n_cov_xy = m(0, 1); n_cov_xz = m(0, 2); n_cov_yy = m(1, 1); n_cov_yz = m(1, 2); n_cov_zz = m(2, 2); } void updateCov(const ikdTree_PointType& p) { // inject small-scale to large-scale } ///code from VoxelMap void calcBodyCovBearingRange(const float range_cov, const float degree_cov) { // if z=0, error will occur in calcBodyCov. To be solved float z_tmp = z; if (z == 0) { z_tmp = 0.001; } float range = sqrt(x * x + y * y + z_tmp * z_tmp); float range_var = range_cov * range_cov; Eigen::Matrix2f direction_var; // (angle_cov^2, 0, // 0, angle_cov^2) direction_var << pow(sin(DEG2RAD(degree_cov)), 2), 0, 0, pow(sin(DEG2RAD(degree_cov)), 2); Eigen::Vector3f direction = this->getVector3fMap(); direction.normalize(); Eigen::Matrix3f direction_hat; // w^ direction_hat << 0, -direction(2), direction(1), direction(2), 0, -direction(0), -direction(1), direction(0), 0; //direction dot base_vector1 = 0 Eigen::Vector3f base_vector1(1, 1, -(direction(0) + direction(1)) / direction(2)); //(1, 1, -(x+y)/z), not unique base_vector1.normalize(); Eigen::Vector3f base_vector2 = base_vector1.cross(direction); base_vector2.normalize(); Eigen::Matrix N; //N = [base_vector1, base_vector2] N << base_vector1(0), base_vector2(0), base_vector1(1), base_vector2(1), base_vector1(2), base_vector2(2); Eigen::Matrix A = range * direction_hat * N; // (d * w^ * N )in the paper //cov = w * var_d * w^T + A * var_w * A^T Eigen::Matrix3f cov = direction * range_var * direction.transpose() + A * direction_var * A.transpose(); recordPointCovFromMatrix(cov); }; } EIGEN_ALIGN16; POINT_CLOUD_REGISTER_POINT_STRUCT (ikdTree_PointType, (float, x, x) (float, y, y) (float, z, z) (float, normal_x, normal_x) (float, normal_y, normal_y) (float, normal_z, normal_z) (float, intensity, intensity) (float, curvature, curvature) (float, s_xx, s_xx) (float, s_xy, s_xy) (float, s_xz, s_xz) (float, s_yy, s_yy) (float, s_yz, s_yz) (float, s_zz, s_zz) (int, num_hit, num_hit) (float, mean_x, mean_x) (float, mean_y, mean_y) (float, mean_z, mean_z) ) // Velodyne struct PointXYZIRT { PCL_ADD_POINT4D PCL_ADD_INTENSITY; uint16_t ring; float time; EIGEN_MAKE_ALIGNED_OPERATOR_NEW } EIGEN_ALIGN16; POINT_CLOUD_REGISTER_POINT_STRUCT (PointXYZIRT, (float, x, x) (float, y, y) (float, z, z) (float, intensity, intensity) (uint16_t, ring, ring) (float, time, time) ) // Ouster struct ousterPointXYZIRT { PCL_ADD_POINT4D; PCL_ADD_INTENSITY; uint32_t t; uint16_t reflectivity; uint8_t ring; uint16_t noise; uint32_t range; EIGEN_MAKE_ALIGNED_OPERATOR_NEW }EIGEN_ALIGN16; POINT_CLOUD_REGISTER_POINT_STRUCT(ousterPointXYZIRT, (float, x, x) (float, y, y) (float, z, z) (float, intensity, intensity) (uint32_t, t, t) (uint16_t, reflectivity, reflectivity) (uint8_t, ring, ring) (uint16_t, noise, noise) (uint32_t, range, range) ) #endif //LIO_POINT_TYPE_H ================================================ FILE: include/so3_math.h ================================================ #ifndef SO3_MATH_H #define SO3_MATH_H #include #include #define SKEW_SYM_MATRX(v) 0.0,-v[2],v[1],v[2],0.0,-v[0],-v[1],v[0],0.0 template Eigen::Matrix skew_sym_mat(const Eigen::Matrix &v) { Eigen::Matrix skew_sym_mat; skew_sym_mat<<0.0,-v[2],v[1],v[2],0.0,-v[0],-v[1],v[0],0.0; return skew_sym_mat; } template Eigen::Matrix Exp(const Eigen::Matrix &&ang) { T ang_norm = ang.norm(); Eigen::Matrix Eye3 = Eigen::Matrix::Identity(); if (ang_norm > 0.0000001) { Eigen::Matrix r_axis = ang / ang_norm; Eigen::Matrix K; K << SKEW_SYM_MATRX(r_axis); /// Roderigous Tranformation return Eye3 + std::sin(ang_norm) * K + (1.0 - std::cos(ang_norm)) * K * K; } else { return Eye3; } } template Eigen::Matrix Exp(const Eigen::Matrix &ang_vel, const Ts &dt) { T ang_vel_norm = ang_vel.norm(); Eigen::Matrix Eye3 = Eigen::Matrix::Identity(); if (ang_vel_norm > 0.0000001) { Eigen::Matrix r_axis = ang_vel / ang_vel_norm; Eigen::Matrix K; K << SKEW_SYM_MATRX(r_axis); T r_ang = ang_vel_norm * dt; /// Roderigous Tranformation return Eye3 + std::sin(r_ang) * K + (1.0 - std::cos(r_ang)) * K * K; } else { return Eye3; } } template Eigen::Matrix Exp(const T &v1, const T &v2, const T &v3) { T &&norm = sqrt(v1 * v1 + v2 * v2 + v3 * v3); Eigen::Matrix Eye3 = Eigen::Matrix::Identity(); if (norm > 0.00001) { T r_ang[3] = {v1 / norm, v2 / norm, v3 / norm}; Eigen::Matrix K; K << SKEW_SYM_MATRX(r_ang); /// Roderigous Tranformation return Eye3 + std::sin(norm) * K + (1.0 - std::cos(norm)) * K * K; } else { return Eye3; } } /* Logrithm of a Rotation Matrix */ template Eigen::Matrix Log(const Eigen::Matrix &R) { T theta = (R.trace() > 3.0 - 1e-6) ? 0.0 : std::acos(0.5 * (R.trace() - 1)); Eigen::Matrix K(R(2,1) - R(1,2), R(0,2) - R(2,0), R(1,0) - R(0,1)); return (std::abs(theta) < 0.001) ? (0.5 * K) : (0.5 * theta / std::sin(theta) * K); } template Eigen::Matrix RotMtoEuler(const Eigen::Matrix &rot) { T sy = sqrt(rot(0,0)*rot(0,0) + rot(1,0)*rot(1,0)); bool singular = sy < 1e-6; T x, y, z; if(!singular) { x = atan2(rot(2, 1), rot(2, 2)); y = atan2(-rot(2, 0), sy); z = atan2(rot(1, 0), rot(0, 0)); } else { x = atan2(-rot(1, 2), rot(1, 1)); y = atan2(-rot(2, 0), sy); z = 0; } Eigen::Matrix ang(x, y, z); return ang; } #endif ================================================ FILE: include/use-ikfom.hpp ================================================ #ifndef USE_IKFOM_H #define USE_IKFOM_H #include typedef MTK::vect<3, double> vect3; typedef MTK::SO3 SO3; typedef MTK::S2 S2; typedef MTK::vect<1, double> vect1; typedef MTK::vect<2, double> vect2; MTK_BUILD_MANIFOLD(state_ikfom, ((vect3, pos)) ((SO3, rot)) ((SO3, offset_R_L_I)) ((vect3, offset_T_L_I)) ((vect3, vel)) ((vect3, bg)) ((vect3, ba)) ((S2, grav)) ); MTK_BUILD_MANIFOLD(input_ikfom, ((vect3, acc)) ((vect3, gyro)) ); MTK_BUILD_MANIFOLD(process_noise_ikfom, ((vect3, ng)) ((vect3, na)) ((vect3, nbg)) ((vect3, nba)) ); MTK::get_cov::type process_noise_cov() { MTK::get_cov::type cov = MTK::get_cov::type::Zero(); MTK::setDiagonal(cov, &process_noise_ikfom::ng, 0.0001);// 0.03 MTK::setDiagonal(cov, &process_noise_ikfom::na, 0.0001); // *dt 0.01 0.01 * dt * dt 0.05 MTK::setDiagonal(cov, &process_noise_ikfom::nbg, 0.00001); // *dt 0.00001 0.00001 * dt *dt 0.3 //0.001 0.0001 0.01 MTK::setDiagonal(cov, &process_noise_ikfom::nba, 0.00001); //0.001 0.05 0.0001/out 0.01 return cov; } //double L_offset_to_I[3] = {0.04165, 0.02326, -0.0284}; // Avia //vect3 Lidar_offset_to_IMU(L_offset_to_I, 3); Eigen::Matrix get_f(state_ikfom &s, const input_ikfom &in) { Eigen::Matrix res = Eigen::Matrix::Zero(); vect3 omega; in.gyro.boxminus(omega, s.bg); vect3 a_inertial = s.rot * (in.acc-s.ba); for(int i = 0; i < 3; i++ ){ res(i) = s.vel[i]; res(i + 3) = omega[i]; res(i + 12) = a_inertial[i] + s.grav[i]; } return res; } Eigen::Matrix df_dx(state_ikfom &s, const input_ikfom &in) { Eigen::Matrix cov = Eigen::Matrix::Zero(); cov.template block<3, 3>(0, 12) = Eigen::Matrix3d::Identity(); vect3 acc_; in.acc.boxminus(acc_, s.ba); vect3 omega; in.gyro.boxminus(omega, s.bg); cov.template block<3, 3>(12, 3) = -s.rot.toRotationMatrix()*MTK::hat(acc_); cov.template block<3, 3>(12, 18) = -s.rot.toRotationMatrix(); Eigen::Matrix vec = Eigen::Matrix::Zero(); Eigen::Matrix grav_matrix; s.S2_Mx(grav_matrix, vec, 21); cov.template block<3, 2>(12, 21) = grav_matrix; cov.template block<3, 3>(3, 15) = -Eigen::Matrix3d::Identity(); return cov; } Eigen::Matrix df_dw(state_ikfom &s, const input_ikfom &in) { Eigen::Matrix cov = Eigen::Matrix::Zero(); cov.template block<3, 3>(12, 3) = -s.rot.toRotationMatrix(); cov.template block<3, 3>(3, 0) = -Eigen::Matrix3d::Identity(); cov.template block<3, 3>(15, 6) = Eigen::Matrix3d::Identity(); cov.template block<3, 3>(18, 9) = Eigen::Matrix3d::Identity(); return cov; } vect3 SO3ToEuler(const SO3 &orient) { Eigen::Matrix _ang; Eigen::Vector4d q_data = orient.coeffs().transpose(); //scalar w=orient.coeffs[3], x=orient.coeffs[0], y=orient.coeffs[1], z=orient.coeffs[2]; double sqw = q_data[3]*q_data[3]; double sqx = q_data[0]*q_data[0]; double sqy = q_data[1]*q_data[1]; double sqz = q_data[2]*q_data[2]; double unit = sqx + sqy + sqz + sqw; // if normalized is one, otherwise is correction factor double test = q_data[3]*q_data[1] - q_data[2]*q_data[0]; if (test > 0.49999*unit) { // singularity at north pole _ang << 2 * std::atan2(q_data[0], q_data[3]), M_PI/2, 0; double temp[3] = {_ang[0] * 57.3, _ang[1] * 57.3, _ang[2] * 57.3}; vect3 euler_ang(temp, 3); return euler_ang; } if (test < -0.49999*unit) { // singularity at south pole _ang << -2 * std::atan2(q_data[0], q_data[3]), -M_PI/2, 0; double temp[3] = {_ang[0] * 57.3, _ang[1] * 57.3, _ang[2] * 57.3}; vect3 euler_ang(temp, 3); return euler_ang; } _ang << std::atan2(2*q_data[0]*q_data[3]+2*q_data[1]*q_data[2] , -sqx - sqy + sqz + sqw), std::asin (2*test/unit), std::atan2(2*q_data[2]*q_data[3]+2*q_data[1]*q_data[0] , sqx - sqy - sqz + sqw); double temp[3] = {_ang[0] * 57.3, _ang[1] * 57.3, _ang[2] * 57.3}; vect3 euler_ang(temp, 3); // euler_ang[0] = roll, euler_ang[1] = pitch, euler_ang[2] = yaw return euler_ang; } #endif ================================================ FILE: include/voxel_map_util.hpp ================================================ #ifndef VOXEL_MAP_UTIL_HPP #define VOXEL_MAP_UTIL_HPP //#include "common_lib.h" #include "omp.h" #include #include #include //#include #include #include #include #include #include #include #include #include #include #include #include "ikd-Tree/ikd_Tree.h" using std::hash; using std::vector; using std::unordered_map; using std::cout; using std::printf; using std::mutex; #define HASH_P 116101 #define MAX_N 10000000000 static int plane_id = 0; // a point to plane matching structure typedef struct ptpl { Eigen::Vector3d point; Eigen::Vector3d point_world; Eigen::Vector3d normal; Eigen::Vector3d center; Eigen::Matrix plane_cov; double d; int layer; Eigen::Matrix3d cov_lidar; } ptpl; // 3D point with covariance typedef struct pointWithCov { Eigen::Vector3d point; Eigen::Vector3d point_world; Eigen::Matrix3d cov; Eigen::Matrix3d cov_lidar; } pointWithCov; typedef struct Plane { Eigen::Vector3f center; Eigen::Vector3f normal; Eigen::Vector3f y_normal; //evalsMid Eigen::Vector3f x_normal; //evalsMax Eigen::Matrix3f covariance; Eigen::Matrix plane_cov; //cov (n, q), q = mean_p float radius = 0; // sqrt(evalsReal(evalsMax)) float min_eigen_value = 1; float mid_eigen_value = 1; float max_eigen_value = 1; float d = 0; // plane: n * p + d = 0 int points_size = 0; bool is_plane = false; bool is_init = false; int id; // is_update and last_update_points_size are only for publish plane bool is_update = false; int last_update_points_size = 0; bool update_enable = true; } Plane; class VoxelInfo; class VOXEL_LOC { public: int64_t x, y, z; VOXEL_LOC(int64_t vx = 0, int64_t vy = 0, int64_t vz = 0) : x(vx), y(vy), z(vz) {} VoxelInfo* info_ptr; bool operator==(const VOXEL_LOC &other) const { return (x == other.x && y == other.y && z == other.z); } }; // Hash value namespace std { template<> struct hash { int64_t operator()(const VOXEL_LOC &s) const { using std::hash; using std::size_t; return ((((s.z) * HASH_P) % MAX_N + (s.y)) * HASH_P) % MAX_N + (s.x); } }; } // namespace std class OctoTree { public: vector temp_points_; // all points in an octo tree vector new_points_; // new points in an octo tree Plane *plane_ptr_; int max_layer_; bool indoor_mode_; int layer_; int octo_state_; // 0 is end of tree, 1 is not OctoTree *leaves_[8]; double voxel_center_[3]; // x, y, z vector layer_point_size_; float quater_length_; float planer_threshold_; int max_plane_update_threshold_; int update_size_threshold_; int all_points_num_; int new_points_num_; int max_points_size_; int max_cov_points_size_; bool init_octo_; bool update_cov_enable_; bool update_enable_; OctoTree(int max_layer, int layer, vector layer_point_size, int max_point_size, int max_cov_points_size, float planer_threshold) : max_layer_(max_layer), layer_(layer), layer_point_size_(layer_point_size), max_points_size_(max_point_size), max_cov_points_size_(max_cov_points_size), planer_threshold_(planer_threshold) { temp_points_.clear(); octo_state_ = 0; new_points_num_ = 0; all_points_num_ = 0; // when new points num > 5, do a update update_size_threshold_ = 5; init_octo_ = false; update_enable_ = true; update_cov_enable_ = true; max_plane_update_threshold_ = layer_point_size_[layer_]; for (int i = 0; i < 8; i++) { leaves_[i] = nullptr; } plane_ptr_ = new Plane; } }; #endif ================================================ FILE: launch/gdb_debug_example.launch ================================================ ================================================ FILE: launch/mapping_m2dgr.launch ================================================ ================================================ FILE: launch/mapping_ouster64.launch ================================================ ================================================ FILE: launch/mapping_rs.launch ================================================ ================================================ FILE: launch/mapping_velodyne.launch ================================================ ================================================ FILE: launch/mapping_viral.launch ================================================ ================================================ FILE: msg/Pose6D.msg ================================================ # the preintegrated Lidar states at the time of IMU measurements in a frame float64 offset_time # the offset time of IMU measurement w.r.t the first lidar point float64[3] acc # the preintegrated total acceleration (global frame) at the Lidar origin float64[3] gyr # the unbiased angular velocity (body frame) at the Lidar origin float64[3] vel # the preintegrated velocity (global frame) at the Lidar origin float64[3] pos # the preintegrated position (global frame) at the Lidar origin float64[9] rot # the preintegrated rotation (global frame) at the Lidar origin ================================================ FILE: package.xml ================================================ log_lio 0.0.0 This is a modified version of LOAM which is original algorithm is described in the following paper: J. Zhang and S. Singh. LOAM: Lidar Odometry and Mapping in Real-time. Robotics: Science and Systems Conference (RSS). Berkeley, CA, July 2014. claydergc BSD Ji Zhang catkin geometry_msgs nav_msgs roscpp rospy std_msgs sensor_msgs tf pcl_ros livox_ros_driver message_generation geometry_msgs nav_msgs sensor_msgs roscpp rospy std_msgs tf pcl_ros livox_ros_driver message_runtime rostest rosbag ================================================ FILE: src/IMU_Processing.hpp ================================================ #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include "use-ikfom.hpp" /// *************Preconfiguration #define MAX_INI_COUNT (10) const bool time_list(PointType &x, PointType &y) {return (x.curvature < y.curvature);}; /// *************IMU Process and undistortion class ImuProcess { public: EIGEN_MAKE_ALIGNED_OPERATOR_NEW ImuProcess(); ~ImuProcess(); void Reset(); void Reset(double start_timestamp, const sensor_msgs::ImuConstPtr &lastimu); void set_extrinsic(const V3D &transl, const M3D &rot); void set_extrinsic(const V3D &transl); void set_extrinsic(const MD(4,4) &T); void set_gyr_cov(const V3D &scaler); void set_acc_cov(const V3D &scaler); void set_gyr_bias_cov(const V3D &b_g); void set_acc_bias_cov(const V3D &b_a); Eigen::Matrix Q; void Process(const MeasureGroup &meas, esekfom::esekf &kf_state, PointCloudXYZI::Ptr pcl_un_); ofstream fout_imu; V3D cov_acc; //todo V3D cov_gyr; V3D cov_acc_scale; V3D cov_gyr_scale; V3D cov_bias_gyr; V3D cov_bias_acc; double first_lidar_time; private: void IMU_init(const MeasureGroup &meas, esekfom::esekf &kf_state, int &N); void UndistortPcl(const MeasureGroup &meas, esekfom::esekf &kf_state, PointCloudXYZI &pcl_in_out); PointCloudXYZI::Ptr cur_pcl_un_; sensor_msgs::ImuConstPtr last_imu_; deque v_imu_; vector IMUpose; vector v_rot_pcl_; M3D Lidar_R_wrt_IMU; V3D Lidar_T_wrt_IMU; V3D mean_acc; V3D mean_gyr; V3D angvel_last; V3D acc_s_last; double start_timestamp_; double last_lidar_end_time_; int init_iter_num = 1; bool b_first_frame_ = true; bool imu_need_init_ = true; }; ImuProcess::ImuProcess() : b_first_frame_(true), imu_need_init_(true), start_timestamp_(-1) { init_iter_num = 1; Q = process_noise_cov(); cov_acc = V3D(0.1, 0.1, 0.1); cov_gyr = V3D(0.1, 0.1, 0.1); cov_bias_gyr = V3D(0.0001, 0.0001, 0.0001); cov_bias_acc = V3D(0.0001, 0.0001, 0.0001); mean_acc = V3D(0, 0, -1.0); mean_gyr = V3D(0, 0, 0); angvel_last = Zero3d; Lidar_T_wrt_IMU = Zero3d; Lidar_R_wrt_IMU = Eye3d; last_imu_.reset(new sensor_msgs::Imu()); } ImuProcess::~ImuProcess() {} void ImuProcess::Reset() { // ROS_WARN("Reset ImuProcess"); mean_acc = V3D(0, 0, -1.0); mean_gyr = V3D(0, 0, 0); angvel_last = Zero3d; imu_need_init_ = true; start_timestamp_ = -1; init_iter_num = 1; v_imu_.clear(); IMUpose.clear(); last_imu_.reset(new sensor_msgs::Imu()); cur_pcl_un_.reset(new PointCloudXYZI()); } void ImuProcess::set_extrinsic(const MD(4,4) &T) { Lidar_T_wrt_IMU = T.block<3,1>(0,3); Lidar_R_wrt_IMU = T.block<3,3>(0,0); } void ImuProcess::set_extrinsic(const V3D &transl) { Lidar_T_wrt_IMU = transl; Lidar_R_wrt_IMU.setIdentity(); } void ImuProcess::set_extrinsic(const V3D &transl, const M3D &rot) { Lidar_T_wrt_IMU = transl; Lidar_R_wrt_IMU = rot; } void ImuProcess::set_gyr_cov(const V3D &scaler) { cov_gyr_scale = scaler; } void ImuProcess::set_acc_cov(const V3D &scaler) { cov_acc_scale = scaler; } void ImuProcess::set_gyr_bias_cov(const V3D &b_g) { cov_bias_gyr = b_g; } void ImuProcess::set_acc_bias_cov(const V3D &b_a) { cov_bias_acc = b_a; } void ImuProcess::IMU_init(const MeasureGroup &meas, esekfom::esekf &kf_state, int &N) { /** 1. initializing the gravity, gyro bias, acc and gyro covariance ** 2. normalize the acceleration measurenments to unit gravity **/ V3D cur_acc, cur_gyr; if (b_first_frame_) { Reset(); N = 1; b_first_frame_ = false; const auto &imu_acc = meas.imu.front()->linear_acceleration; const auto &gyr_acc = meas.imu.front()->angular_velocity; mean_acc << imu_acc.x, imu_acc.y, imu_acc.z; mean_gyr << gyr_acc.x, gyr_acc.y, gyr_acc.z; first_lidar_time = meas.lidar_beg_time; } for (const auto &imu : meas.imu) { const auto &imu_acc = imu->linear_acceleration; const auto &gyr_acc = imu->angular_velocity; cur_acc << imu_acc.x, imu_acc.y, imu_acc.z; cur_gyr << gyr_acc.x, gyr_acc.y, gyr_acc.z; mean_acc += (cur_acc - mean_acc) / N; mean_gyr += (cur_gyr - mean_gyr) / N; cov_acc = cov_acc * (N - 1.0) / N + (cur_acc - mean_acc).cwiseProduct(cur_acc - mean_acc) * (N - 1.0) / (N * N); cov_gyr = cov_gyr * (N - 1.0) / N + (cur_gyr - mean_gyr).cwiseProduct(cur_gyr - mean_gyr) * (N - 1.0) / (N * N); // cout<<"acc norm: "<::cov init_P = kf_state.get_P(); init_P.setIdentity(); init_P(6,6) = init_P(7,7) = init_P(8,8) = 0.00001; init_P(9,9) = init_P(10,10) = init_P(11,11) = 0.00001; init_P(15,15) = init_P(16,16) = init_P(17,17) = 0.0001; init_P(18,18) = init_P(19,19) = init_P(20,20) = 0.001; init_P(21,21) = init_P(22,22) = 0.00001; kf_state.change_P(init_P); last_imu_ = meas.imu.back(); } void ImuProcess::UndistortPcl(const MeasureGroup &meas, esekfom::esekf &kf_state, PointCloudXYZI &pcl_out) { /*** add the imu of the last frame-tail to the of current frame-head ***/ auto v_imu = meas.imu; v_imu.push_front(last_imu_); const double &imu_beg_time = v_imu.front()->header.stamp.toSec(); const double &imu_end_time = v_imu.back()->header.stamp.toSec(); const double &pcl_beg_time = meas.lidar_beg_time; const double &pcl_end_time = meas.lidar_end_time; /*** sort point clouds by offset time ***/ pcl_out = *(meas.lidar); sort(pcl_out.points.begin(), pcl_out.points.end(), time_list); // cout<<"[ IMU Process ]: Process lidar from "<header.stamp.toSec() < last_lidar_end_time_) continue; angvel_avr<<0.5 * (head->angular_velocity.x + tail->angular_velocity.x), 0.5 * (head->angular_velocity.y + tail->angular_velocity.y), 0.5 * (head->angular_velocity.z + tail->angular_velocity.z); acc_avr <<0.5 * (head->linear_acceleration.x + tail->linear_acceleration.x), 0.5 * (head->linear_acceleration.y + tail->linear_acceleration.y), 0.5 * (head->linear_acceleration.z + tail->linear_acceleration.z); // fout_imu << setw(10) << head->header.stamp.toSec() - first_lidar_time << " " << angvel_avr.transpose() << " " << acc_avr.transpose() << endl; acc_avr = acc_avr * G_m_s2 / mean_acc.norm(); // - state_inout.ba; if(head->header.stamp.toSec() < last_lidar_end_time_) { dt = tail->header.stamp.toSec() - last_lidar_end_time_; // dt = tail->header.stamp.toSec() - pcl_beg_time; } else { dt = tail->header.stamp.toSec() - head->header.stamp.toSec(); } in.acc = acc_avr; in.gyro = angvel_avr; Q.block<3, 3>(0, 0).diagonal() = cov_gyr; Q.block<3, 3>(3, 3).diagonal() = cov_acc; Q.block<3, 3>(6, 6).diagonal() = cov_bias_gyr; Q.block<3, 3>(9, 9).diagonal() = cov_bias_acc; kf_state.predict(dt, Q, in); /* save the poses at each IMU measurements */ imu_state = kf_state.get_x(); angvel_last = angvel_avr - imu_state.bg; acc_s_last = imu_state.rot * (acc_avr - imu_state.ba); for(int i=0; i<3; i++) { acc_s_last[i] += imu_state.grav[i]; } double &&offs_t = tail->header.stamp.toSec() - pcl_beg_time; IMUpose.push_back(set_pose6d(offs_t, acc_s_last, angvel_last, imu_state.vel, imu_state.pos, imu_state.rot.toRotationMatrix())); } /*** calculated the pos and attitude prediction at the frame-end ***/ double note = pcl_end_time > imu_end_time ? 1.0 : -1.0; dt = note * (pcl_end_time - imu_end_time); kf_state.predict(dt, Q, in); imu_state = kf_state.get_x(); last_imu_ = meas.imu.back(); last_lidar_end_time_ = pcl_end_time; /*** undistort each lidar point (backward propagation) ***/ if (pcl_out.points.begin() == pcl_out.points.end()) return; auto it_pcl = pcl_out.points.end() - 1; for (auto it_kp = IMUpose.end() - 1; it_kp != IMUpose.begin(); it_kp--) { auto head = it_kp - 1; auto tail = it_kp; R_imu<rot); // cout<<"head imu acc: "<vel); pos_imu<pos); acc_imu<acc); angvel_avr<gyr); for(; it_pcl->curvature / double(1000) > head->offset_time; it_pcl --) { dt = it_pcl->curvature / double(1000) - head->offset_time; /* Transform to the 'end' frame, using only the rotation * Note: Compensation direction is INVERSE of Frame's moving direction * So if we want to compensate a point at timestamp-i to the frame-e * P_compensate = R_imu_e ^ T * (R_i * P_i + T_ei) where T_ei is represented in global frame */ M3D R_i(R_imu * Exp(angvel_avr, dt)); V3D P_i(it_pcl->x, it_pcl->y, it_pcl->z); V3D T_ei(pos_imu + vel_imu * dt + 0.5 * acc_imu * dt * dt - imu_state.pos); V3D P_compensate = imu_state.offset_R_L_I.conjugate() * (imu_state.rot.conjugate() * (R_i * (imu_state.offset_R_L_I * P_i + imu_state.offset_T_L_I) + T_ei) - imu_state.offset_T_L_I);// not accurate! V3D normal_i(it_pcl->normal_x, it_pcl->normal_y, it_pcl->normal_z); V3D normal_compensate = imu_state.offset_R_L_I.conjugate() * imu_state.rot.conjugate() * R_i * imu_state.offset_R_L_I * normal_i; // save Undistorted points and their rotation it_pcl->x = P_compensate(0); it_pcl->y = P_compensate(1); it_pcl->z = P_compensate(2); it_pcl->normal_x = normal_compensate(0); it_pcl->normal_y = normal_compensate(1); it_pcl->normal_z = normal_compensate(2); if (it_pcl == pcl_out.points.begin()) break; } } } void ImuProcess::Process(const MeasureGroup &meas, esekfom::esekf &kf_state, PointCloudXYZI::Ptr cur_pcl_un_) { double t1,t2,t3; t1 = omp_get_wtime(); if(meas.imu.empty()) {return;}; ROS_ASSERT(meas.lidar != nullptr); if (imu_need_init_) { /// The very first lidar frame IMU_init(meas, kf_state, init_iter_num); imu_need_init_ = true; last_imu_ = meas.imu.back(); state_ikfom imu_state = kf_state.get_x(); if (init_iter_num > MAX_INI_COUNT) { cov_acc *= pow(G_m_s2 / mean_acc.norm(), 2); imu_need_init_ = false; cov_acc = cov_acc_scale; cov_gyr = cov_gyr_scale; ROS_INFO("IMU Initial Done"); // ROS_INFO("IMU Initial Done: Gravity: %.4f %.4f %.4f %.4f; state.bias_g: %.4f %.4f %.4f; acc covarience: %.8f %.8f %.8f; gry covarience: %.8f %.8f %.8f",\ // imu_state.grav[0], imu_state.grav[1], imu_state.grav[2], mean_acc.norm(), cov_bias_gyr[0], cov_bias_gyr[1], cov_bias_gyr[2], cov_acc[0], cov_acc[1], cov_acc[2], cov_gyr[0], cov_gyr[1], cov_gyr[2]); fout_imu.open(DEBUG_FILE_DIR("imu.txt"),ios::out); } return; } UndistortPcl(meas, kf_state, *cur_pcl_un_); t2 = omp_get_wtime(); t3 = omp_get_wtime(); // cout<<"[ IMU Process ]: Time: "< #include #include extern float mid2min, planarity, angle_threshold; extern int num_surfel, surfel_points_min, surfel_points_max; extern bool check_normal; //const int large_scale_flag = -2; bool cloudSurfel(const vector & voxels, Matrix &pca_result, V3F& centroid, const float& threshold) { float s_xx= 0.0, s_xy = 0.0, s_xz = 0.0, s_yy = 0.0, s_yz = 0.0, s_zz = 0.0; float mean_x = 0.0, mean_y = 0.0, mean_z = 0.0; float num_points = 0.0; // centroid = Eigen::Vector3f (0, 0, 0); Eigen::Vector3f mean_normal(0, 0, 0); vector valid_mean_xyz(voxels.size()); int num_valid = 0; for (int i = 0; i < (int) voxels.size(); ++i) { const auto & v = voxels[i]; if (!v) continue; // lock_guard lock(v->updating_mutex_lock); if (v->num_points >= 0 && v->num_points < surfel_points_min) // not enough points continue; float n = (float)abs(v->num_points); num_points += n; // const PointType & p = cloud[i]; s_xx += v->s_xx; s_xy += v->s_xy; s_xz += v->s_xz; s_yy += v->s_yy; s_yz += v->s_yz; s_zz += v->s_zz; mean_x += n * v->mean_x; mean_y += n * v->mean_y; mean_z += n * v->mean_z; mean_normal += n * v->normal; valid_mean_xyz[num_valid](0) = v->mean_x; valid_mean_xyz[num_valid](1) = v->mean_y; valid_mean_xyz[num_valid](2) = v->mean_z; ++num_valid; } // todo if (num_points < surfel_points_min * NUM_MATCH_POINTS * 0.5) return false; mean_x /= num_points; mean_y /= num_points; mean_z /= num_points; centroid(0) = mean_x; centroid(1) = mean_y; centroid(2) = mean_z; mean_normal = mean_normal / num_points; mean_normal.normalize(); // valid_mean_xyz.resize(num_valid); // ROS_INFO("mean xyz %f %f %f", mean_x, mean_y, mean_z); Eigen::Matrix3f cov; cov(0, 0) = s_xx / num_points - mean_x * mean_x; cov(1, 1) = s_yy / num_points - mean_y * mean_y; cov(2, 2) = s_zz / num_points - mean_z * mean_z; cov(0, 1) = s_xy / num_points - mean_x * mean_y; cov(1, 0) = cov(0, 1); cov(0, 2) = s_xz / num_points - mean_x * mean_z; cov(2, 0) = cov(0, 2); cov(1, 2) = s_yz / num_points - mean_y * mean_z; cov(2, 1) = cov(1, 2); // Compute the product cloud_demean * cloud_demean^T // Eigen::Matrix3f alpha = static_cast (cloud_demean.topRows<3> () * cloud_demean.topRows<3> ().transpose ()); Eigen::Matrix3f eigenvectors_; Eigen::Vector3f eigenvalues_; // Compute eigen vectors and values Eigen::SelfAdjointEigenSolver evd (cov); // Organize eigenvectors and eigenvalues in ascendent order // lambda 1 > lambda 2 > lambda 3 for (int i = 0; i < 3; ++i) { eigenvalues_[i] = evd.eigenvalues () [2-i]; eigenvectors_.col (i) = evd.eigenvectors ().col (2-i); } // float cos = eigenvectors_.col (2).dot(voxels[0]->normal); if (eigenvalues_[0] == eigenvalues_[2] || eigenvalues_[0] == 0.0) return false; float pho = 2.0 * (eigenvalues_[1] - eigenvalues_[2]) / (eigenvalues_[0] + eigenvalues_[1] + eigenvalues_[2]); float mid_min = eigenvalues_[1] / eigenvalues_[2]; // n * p + d = 0; Eigen::Vector3f normal = eigenvectors_.col (2); if (mean_normal.dot(normal) < 0) normal *= -1; pca_result.head(3) = normal; pca_result(3) = -centroid.dot(normal); // d for (int j = 0; j < num_valid; j++) { // while (voxels[j]->flag_updating) /// !!!caution // usleep(2); // pthread_mutex_lock(&voxels[j]->updating_mutex_lock); if (fabs(normal.dot(valid_mean_xyz[j]) + pca_result(3)) > threshold) { return false; } // pthread_mutex_lock(&voxels[j]->updating_mutex_lock); } return true; } ///code from VoxelMap void calcBodyCov(Eigen::Vector3d &pb, const float range_inc, const float degree_inc, Eigen::Matrix3d &cov) { // if z=0, error will occur in calcBodyCov. To be solved float z_tmp = pb[2]; if (z_tmp == 0) { z_tmp = 0.001; } float range = sqrt(pb[0] * pb[0] + pb[1] * pb[1] + z_tmp * z_tmp); // float range = sqrt(pb[0] * pb[0] + pb[1] * pb[1] + pb[2] * pb[2]); float range_var = range_inc * range_inc; Eigen::Matrix2d direction_var; // (angle_cov^2, 0, // 0, angle_cov^2) direction_var << pow(sin(DEG2RAD(degree_inc)), 2), 0, 0, pow(sin(DEG2RAD(degree_inc)), 2); Eigen::Vector3d direction(pb); direction.normalize(); Eigen::Matrix3d direction_hat; // w^ direction_hat << 0, -direction(2), direction(1), direction(2), 0, -direction(0), -direction(1), direction(0), 0; //direction dot base_vector1 = 0 Eigen::Vector3d base_vector1(1, 1, -(direction(0) + direction(1)) / direction(2)); //(1, 1, -(x+y)/z), not unique base_vector1.normalize(); Eigen::Vector3d base_vector2 = base_vector1.cross(direction); base_vector2.normalize(); Eigen::Matrix N; //N = [base_vector1, base_vector2] N << base_vector1(0), base_vector2(0), base_vector1(1), base_vector2(1), base_vector1(2), base_vector2(2); Eigen::Matrix A = range * direction_hat * N; // (d * w^ * N )in the paper //cov = w * var_d * w^T + A * var_w * A^T cov = direction * range_var * direction.transpose() + A * direction_var * A.transpose(); } void calcBodyCov(PointType &pb, const float range_cov, const float degree_cov, Eigen::Matrix3f &cov) { // if z=0, error will occur in calcBodyCov. To be solved float z_tmp = pb.z; if (z_tmp == 0) { z_tmp = 0.001; } float range = sqrt(pb.x* pb.x + pb.y * pb.y + z_tmp * z_tmp); // float range = sqrt(pb.x* pb.x + pb.y * pb.y + pb.z * pb.z); float range_var = range_cov * range_cov; Eigen::Matrix2f direction_var; // (angle_cov^2, 0, // 0, angle_cov^2) direction_var << pow(sin(DEG2RAD(degree_cov)), 2), 0, 0, pow(sin(DEG2RAD(degree_cov)), 2); Eigen::Vector3f direction = pb.getVector3fMap(); direction.normalize(); Eigen::Matrix3f direction_hat; // w^ direction_hat << 0, -direction(2), direction(1), direction(2), 0, -direction(0), -direction(1), direction(0), 0; //direction dot base_vector1 = 0 Eigen::Vector3f base_vector1(1, 1, -(direction(0) + direction(1)) / direction(2)); //(1, 1, -(x+y)/z), not unique base_vector1.normalize(); Eigen::Vector3f base_vector2 = base_vector1.cross(direction); base_vector2.normalize(); Eigen::Matrix N; //N = [base_vector1, base_vector2] N << base_vector1(0), base_vector2(0), base_vector1(1), base_vector2(1), base_vector1(2), base_vector2(2); Eigen::Matrix A = range * direction_hat * N; // (d * w^ * N )in the paper //cov = w * var_d * w^T + A * var_w * A^T cov = direction * range_var * direction.transpose() + A * direction_var * A.transpose(); // pb.recordPointCovFromMatrix(cov); }; void GetUpdatePlane(const OctoTree *current_octo, const int pub_max_voxel_layer, vector &plane_list) { if (current_octo->layer_ > pub_max_voxel_layer) { return; } if (current_octo->plane_ptr_->is_update) { plane_list.push_back(*current_octo->plane_ptr_); } if (current_octo->layer_ < current_octo->max_layer_) { if (!current_octo->plane_ptr_->is_plane) { for (size_t i = 0; i < 8; i++) { if (current_octo->leaves_[i] != nullptr) { GetUpdatePlane(current_octo->leaves_[i], pub_max_voxel_layer, plane_list); } } } } return; } void mapJet(double v, double vmin, double vmax, uint8_t &r, uint8_t &g, uint8_t &b) { r = 255; g = 255; b = 255; if (v < vmin) { v = vmin; } if (v > vmax) { v = vmax; } double dr, dg, db; if (v < 0.1242) { db = 0.504 + ((1. - 0.504) / 0.1242) * v; dg = dr = 0.; } else if (v < 0.3747) { db = 1.; dr = 0.; dg = (v - 0.1242) * (1. / (0.3747 - 0.1242)); } else if (v < 0.6253) { db = (0.6253 - v) * (1. / (0.6253 - 0.3747)); dg = 1.; dr = (v - 0.3747) * (1. / (0.6253 - 0.3747)); } else if (v < 0.8758) { db = 0.; dr = 1.; dg = (0.8758 - v) * (1. / (0.8758 - 0.6253)); } else { db = 0.; dg = 0.; dr = 1. - (v - 0.8758) * ((1. - 0.504) / (1. - 0.8758)); } r = (uint8_t) (255 * dr); g = (uint8_t) (255 * dg); b = (uint8_t) (255 * db); } void CalcVectQuation(const Eigen::Vector3d &x_vec, const Eigen::Vector3d &y_vec, const Eigen::Vector3d &z_vec, geometry_msgs::Quaternion &q) { Eigen::Matrix3d rot; rot << x_vec(0), x_vec(1), x_vec(2), y_vec(0), y_vec(1), y_vec(2), z_vec(0), z_vec(1), z_vec(2); Eigen::Matrix3d rotation = rot.transpose(); Eigen::Quaterniond eq(rotation); q.w = eq.w(); q.x = eq.x(); q.y = eq.y(); q.z = eq.z(); } void CalcQuation(const Eigen::Vector3d &vec, const int axis, geometry_msgs::Quaternion &q) { Eigen::Vector3d x_body = vec; Eigen::Vector3d y_body(1, 1, 0); if (x_body(2) != 0) { y_body(2) = -(y_body(0) * x_body(0) + y_body(1) * x_body(1)) / x_body(2); } else { if (x_body(1) != 0) { y_body(1) = -(y_body(0) * x_body(0)) / x_body(1); } else { y_body(0) = 0; } } y_body.normalize(); Eigen::Vector3d z_body = x_body.cross(y_body); Eigen::Matrix3d rot; rot << x_body(0), x_body(1), x_body(2), y_body(0), y_body(1), y_body(2), z_body(0), z_body(1), z_body(2); Eigen::Matrix3d rotation = rot.transpose(); if (axis == 2) { Eigen::Matrix3d rot_inc; rot_inc << 0, 0, 1, 0, 1, 0, -1, 0, 0; rotation = rotation * rot_inc; } Eigen::Quaterniond eq(rotation); q.w = eq.w(); q.x = eq.x(); q.y = eq.y(); q.z = eq.z(); } M3D calcBodyCov(Eigen::Vector3d &pb, const float range_inc, const float degree_inc) { float range = sqrt(pb[0] * pb[0] + pb[1] * pb[1] + pb[2] * pb[2]); float range_var = range_inc * range_inc; Eigen::Matrix2d direction_var; // (angle_cov^2, 0, // 0, angle_cov^2) direction_var << pow(sin(DEG2RAD(degree_inc)), 2), 0, 0, pow(sin(DEG2RAD(degree_inc)), 2); Eigen::Vector3d direction(pb); direction.normalize(); Eigen::Matrix3d direction_hat; // w^ direction_hat << 0, -direction(2), direction(1), direction(2), 0, -direction(0), -direction(1), direction(0), 0; //direction dot base_vector1 = 0 Eigen::Vector3d base_vector1(1, 1, -(direction(0) + direction(1)) / direction(2)); //(1, 1, -(x+y)/z), not unique base_vector1.normalize(); Eigen::Vector3d base_vector2 = base_vector1.cross(direction); base_vector2.normalize(); Eigen::Matrix N; //N = [base_vector1, base_vector2] N << base_vector1(0), base_vector2(0), base_vector1(1), base_vector2(1), base_vector1(2), base_vector2(2); Eigen::Matrix A = range * direction_hat * N; // (d * w^ * N )in the paper //cov = w * var_d * w^T + A * var_w * A^T return direction * range_var * direction.transpose() + A * direction_var * A.transpose(); };FAST_LIO_CLOUD_PROCESS_HPP bool mergeVoxelCheck(const vector& voxels, const Eigen::Vector3f& body2point, const PointType & point_world, Matrix &pca_plane, const float& threshold) { float sum_diff_angle = 0; // angle between normals int num_fail_normal = 0; float s_xx= 0.0, s_xy = 0.0, s_xz = 0.0, s_yy = 0.0, s_yz = 0.0, s_zz = 0.0; float mean_x = 0.0, mean_y = 0.0, mean_z = 0.0; float num_points = 0.0; int num_valid_voxels = 0; Eigen::Vector3f mean_normal(0, 0, 0); Eigen::Vector3f centroid(0, 0, 0); vector valid_mean_xyz(voxels.size()); for (int j = 0; j < voxels.size(); j++) { //check map points normal direction, to sure lidar can see it if (!voxels[j]) continue; VoxelInfo* v = voxels[j]; // check normal consistency const Eigen::Vector3f& voxel_normal = v->normal; if (check_normal && voxel_normal.dot(body2point) > 0) { ++num_fail_normal; continue; } sum_diff_angle += acos(point_world.getNormalVector3fMap().dot(voxel_normal)); // check enough points in voxel if (v->num_points >= 0 && v->num_points < surfel_points_min) // not enough points continue; float n = (float)abs(v->num_points); num_points += n; // const PointType & p = cloud[i]; s_xx += v->s_xx; s_xy += v->s_xy; s_xz += v->s_xz; s_yy += v->s_yy; s_yz += v->s_yz; s_zz += v->s_zz; mean_x += n * v->mean_x; mean_y += n * v->mean_y; mean_z += n * v->mean_z; mean_normal += n * v->normal; // copy valid mean position valid_mean_xyz[num_valid_voxels](0) = v->mean_x; valid_mean_xyz[num_valid_voxels](1) = v->mean_y; valid_mean_xyz[num_valid_voxels](2) = v->mean_z; ++num_valid_voxels; } mean_x /= num_points; mean_y /= num_points; mean_z /= num_points; centroid(0) = mean_x; centroid(1) = mean_y; centroid(2) = mean_z; mean_normal = mean_normal / num_points; mean_normal.normalize(); Eigen::Matrix3f cov; cov(0, 0) = s_xx - num_points * mean_x * mean_x; cov(1, 1) = s_yy - num_points * mean_y * mean_y; cov(2, 2) = s_zz - num_points * mean_z * mean_z; cov(0, 1) = s_xy - num_points * mean_x * mean_y; cov(1, 0) = cov(0, 1); cov(0, 2) = s_xz - num_points * mean_x * mean_z; cov(2, 0) = cov(0, 2); cov(1, 2) = s_yz - num_points * mean_y * mean_z; cov(2, 1) = cov(1, 2); Eigen::Matrix3f eigenvectors_; Eigen::Vector3f eigenvalues_; // Compute eigen vectors and values Eigen::SelfAdjointEigenSolver evd (cov); // Organize eigenvectors and eigenvalues in ascendent order // lambda 1 > lambda 2 > lambda 3 for (int i = 0; i < 3; ++i) { eigenvalues_[i] = evd.eigenvalues () [2-i]; eigenvectors_.col (i) = evd.eigenvectors ().col (2-i); } if (eigenvalues_[0] == eigenvalues_[2] || eigenvalues_[0] == 0.0) return false; float pho = 2.0 * (eigenvalues_[1] - eigenvalues_[2]) / (eigenvalues_[0] + eigenvalues_[1] + eigenvalues_[2]); float mid_min = eigenvalues_[1] / eigenvalues_[2]; // n * p + d = 0; Eigen::Vector3f normal = eigenvectors_.col (2); if (mean_normal.dot(normal) < 0) normal *= -1; pca_plane.head(3) = normal; pca_plane(3) = -centroid.dot(normal); // d for (const auto & valid_mean : valid_mean_xyz) if (fabs(pca_plane.head(3).dot(valid_mean) + pca_plane(3)) > threshold) return false; V3F cent2point(point_world.getVector3fMap() - centroid); pca_plane(3) = normal.dot(cent2point); //pca_plane(3) record point to surfel distance return true; } bool singleVoxelCheck(const vector& voxels, const Eigen::Vector3f& body2point, const PointType & point_world, Matrix &pca_result, const float& threshold) { // idea from voxelMap double prob = 0.0; float dist_min = 999.99; for (const VoxelInfo* vox : voxels) { if (!vox) continue; if (vox->num_points > 0 && vox->num_points < surfel_points_min) continue; if (check_normal && vox->normal.dot(body2point) > 0) continue; // todo 3 * standard deviation // while (voxel_info->flag_updating) /// !!!caution // usleep(2); // lock_guard lock(voxel_info->updating_mutex_lock); V3F plane_centroid(vox->mean_x, vox->mean_y, vox->mean_z); V3F plane_normal(vox->normal); V3F cent2point(point_world.getVector3fMap() - plane_centroid); float p2surfel = plane_normal.dot(cent2point); float abs_dist = fabs(p2surfel); //todo 从不确定度判断最佳surfel if (abs_dist < threshold && abs_dist < dist_min) { dist_min = abs_dist; pca_result.head(3) = plane_normal; pca_result(3) = p2surfel; //pca_plane(3) record point to surfel distance } } if (dist_min < threshold) return true; return false; } #endif //FAST_LIO_CLOUD_PROCESS_HPP ================================================ FILE: src/laserMapping.cpp ================================================ #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include "IMU_Processing.hpp" #include "preprocess.h" #include "tic_toc.h" #include "cloud_process.hpp" #include "voxel_map_util.hpp" #include #include #include #include #include #include #include #include #include #include #include //#include #include #define INIT_TIME (0.1) #define LASER_POINT_COV (0.001) #define MAXN (720000) #define PUBFRAME_PERIOD (20) /*** Time Log Variables ***/ double kdtree_incremental_time = 0.0, kdtree_search_time = 0.0, kdtree_delete_time = 0.0; double T1[MAXN], s_plot[MAXN], s_plot2[MAXN], s_plot3[MAXN], s_plot4[MAXN], s_plot5[MAXN], s_plot6[MAXN], s_plot7[MAXN], s_plot8[MAXN], s_plot9[MAXN], s_plot10[MAXN], s_plot11[MAXN]; double match_time = 0, solve_time = 0, solve_const_H_time = 0; int kdtree_size_st = 0, kdtree_size_end = 0, add_point_size = 0, kdtree_delete_counter = 0; bool runtime_pos_log = false, pcd_save_en = false, time_sync_en = false, extrinsic_est_en = true, path_en = true; /**************************/ float res_last[100000] = {0.0}; float DET_RANGE = 300.0f; const float MOV_THRESHOLD = 1.5f; double time_diff_lidar_to_imu = 0.0; mutex mtx_buffer; condition_variable sig_buffer; string root_dir = ROOT_DIR; string map_file_path, lid_topic, imu_topic; double res_mean_last = 0.05, total_residual = 0.0; double last_timestamp_lidar = 0, last_timestamp_imu = -1.0; double gyr_cov = 0.1, acc_cov = 0.1, b_gyr_cov = 0.0001, b_acc_cov = 0.0001; double filter_size_corner_min = 0, filter_size_surf_min = 0, filter_size_map_min = 0, fov_deg = 0, half_map_size = 0; double cube_len = 0, HALF_FOV_COS = 0, FOV_DEG = 0, total_distance = 0, lidar_end_time = 0, first_lidar_time = 0.0; int effct_feat_num = 0, time_log_counter = 0, scan_count = 0, publish_count = 0; int iterCount = 0, feats_down_size = 0, NUM_MAX_ITERATIONS = 0, laserCloudValidNum = 0, pcd_save_interval = -1, pcd_index = 0; bool point_selected_surf[100000] = {0}; bool lidar_pushed, flg_first_scan = true, flg_exit = false, flg_EKF_inited; bool scan_pub_en = false, dense_pub_en = false, scan_body_pub_en = false; vector> pointSearchInd_surf; vector cub_needrm; vector Nearest_Points; vector::VoxelInfoPtr>> Nearest_Nodes; vector extrinT(3, 0.0); vector extrinR(9, 0.0); deque time_buffer; deque lidar_buffer; deque imu_buffer; PointCloudXYZI::Ptr featsFromMap(new PointCloudXYZI()); PointCloudXYZI::Ptr feats_undistort(new PointCloudXYZI()); PointCloudXYZI::Ptr feats_down_body(new PointCloudXYZI()); PointCloudXYZI::Ptr feats_down_world(new PointCloudXYZI()); PointCloudXYZI::Ptr normvec(new PointCloudXYZI(100000, 1)); PointCloudXYZI::Ptr laserCloudOri(new PointCloudXYZI(100000, 1)); PointCloudXYZI::Ptr corr_normvect(new PointCloudXYZI(100000, 1)); PointCloudXYZI::Ptr _featsArray; PointCloudXYZI::Ptr cloud_with_normal; pcl::VoxelGrid downSizeFilterSurf; pcl::VoxelGrid downSizeFilterMap; //KD_TREE ikdtree; KD_TREE ikdtree; V3F XAxisPoint_body(LIDAR_SP_LEN, 0.0, 0.0); V3F XAxisPoint_world(LIDAR_SP_LEN, 0.0, 0.0); V3D euler_cur; V3D position_last(Zero3d); V3D Lidar_T_wrt_IMU(Zero3d);//T lidar to imu (imu = r * lidar + t) M3D Lidar_R_wrt_IMU(Eye3d);//R lidar to imu (imu = r * lidar) double lidar_time_offset = 0.0; // record begin time for pose interpolation deque timestamps_lidar; nav_msgs::Path path_target_begin, path_target_end; /*** EKF inputs and output ***/ MeasureGroup Measures; esekfom::esekf kf; state_ikfom state_point; //Eigen::VectorXf body_keypose_last(6); //rpy xyz state_ikfom state_body_last; vect3 pos_lid; geometry_msgs::PoseStamped msg_target_pose; bool have_keyframe = false; bool save_ikdtree_map; bool save_final_map; int num_surfel = 0; nav_msgs::Path path; nav_msgs::Path path_target; nav_msgs::Odometry odomAftMapped; geometry_msgs::Quaternion geoQuat; geometry_msgs::PoseStamped msg_body_pose; shared_ptr p_pre(new Preprocess()); shared_ptr p_imu(new ImuProcess()); // parameters for normal estimation int N_SCAN, Horizon_SCAN; string ring_table_dir; bool check_normal; // surfel parameters float planarity = 1.0, mid2min = 100.0; int surfel_points_min = 20; int surfel_points_max = 100; float angle_threshold = 10.0; bool cloud_surfel = true; bool point_surfel = true; int num_cloud_surfel = 0, num_point_surfel = 0; //voxel map std::unordered_map voxel_map; int max_cov_points_size = 50; int max_points_size = 50; double sigma_num = 2.0; double max_voxel_size = 1.0; std::vector layer_size; double min_eigen_value = 0.003; int max_layer = 0; std::vector layer_point_size; bool voxel_hash_en = true; // for ground truth, target in IMU frame vector gt_extrinT(3, 0.0); vector gt_extrinR(9, 0.0); V3D gt_T_wrt_IMU(Zero3d); M3D gt_R_wrt_IMU(Eye3d); void SigHandle(int sig) { flg_exit = true; ROS_WARN("catch sig %d", sig); sig_buffer.notify_all(); } inline void dump_lio_state_to_log(FILE *fp) { V3D rot_ang(Log(state_point.rot.toRotationMatrix())); fprintf(fp, "%lf ", Measures.lidar_beg_time - first_lidar_time); fprintf(fp, "%lf %lf %lf ", rot_ang(0), rot_ang(1), rot_ang(2)); // Angle fprintf(fp, "%lf %lf %lf ", state_point.pos(0), state_point.pos(1), state_point.pos(2)); // Pos fprintf(fp, "%lf %lf %lf ", 0.0, 0.0, 0.0); // omega fprintf(fp, "%lf %lf %lf ", state_point.vel(0), state_point.vel(1), state_point.vel(2)); // Vel fprintf(fp, "%lf %lf %lf ", 0.0, 0.0, 0.0); // Acc fprintf(fp, "%lf %lf %lf ", state_point.bg(0), state_point.bg(1), state_point.bg(2)); // Bias_g fprintf(fp, "%lf %lf %lf ", state_point.ba(0), state_point.ba(1), state_point.ba(2)); // Bias_a fprintf(fp, "%lf %lf %lf ", state_point.grav[0], state_point.grav[1], state_point.grav[2]); // Bias_a fprintf(fp, "\r\n"); fflush(fp); } void pointBodyToWorld_ikfom(PointType const * const pi, PointType * const po, state_ikfom &s) { V3D p_body(pi->x, pi->y, pi->z); V3D p_global(s.rot * (s.offset_R_L_I*p_body + s.offset_T_L_I) + s.pos); po->x = p_global(0); po->y = p_global(1); po->z = p_global(2); po->intensity = pi->intensity; } void pointBodyToWorld(PointType const * const pi, PointType * const po) { V3D p_body(pi->x, pi->y, pi->z); //world <-- imu <-- lidar V3D p_global(state_point.rot * (state_point.offset_R_L_I*p_body + state_point.offset_T_L_I) + state_point.pos); po->x = p_global(0); po->y = p_global(1); po->z = p_global(2); po->intensity = pi->intensity; } void pointNormalBodyToWorld(PointType const * const pi, PointType * const po) { pointBodyToWorld(pi, po); // normal V3F normal_global(state_point.rot.cast() * state_point.offset_R_L_I.cast() * pi->getNormalVector3fMap()); po->normal_x = normal_global(0); po->normal_y = normal_global(1); po->normal_z = normal_global(2); } template void pointBodyToWorld(const Matrix &pi, Matrix &po) { V3D p_body(pi[0], pi[1], pi[2]); V3D p_global(state_point.rot * (state_point.offset_R_L_I*p_body + state_point.offset_T_L_I) + state_point.pos); po[0] = p_global(0); po[1] = p_global(1); po[2] = p_global(2); } void RGBpointBodyToWorld(PointType const * const pi, PointType * const po) { V3D p_body(pi->x, pi->y, pi->z); V3D p_global(state_point.rot * (state_point.offset_R_L_I*p_body + state_point.offset_T_L_I) + state_point.pos); po->x = p_global(0); po->y = p_global(1); po->z = p_global(2); po->intensity = pi->intensity; // normal V3D normal_body(pi->normal_x, pi->normal_y, pi->normal_z); //lidar系下normal V3D normal_global(state_point.rot * state_point.offset_R_L_I * normal_body); //w系下坐标 po->normal_x = normal_global(0); po->normal_y = normal_global(1); po->normal_z = normal_global(2); } void RGBpointBodyLidarToIMU(PointType const * const pi, PointType * const po) { V3D p_body_lidar(pi->x, pi->y, pi->z); V3D p_body_imu(state_point.offset_R_L_I*p_body_lidar + state_point.offset_T_L_I); po->x = p_body_imu(0); po->y = p_body_imu(1); po->z = p_body_imu(2); po->intensity = pi->intensity; } void points_cache_collect() { PointVector points_history; ikdtree.acquire_removed_points(points_history); // for (int i = 0; i < points_history.size(); i++) _featsArray->push_back(points_history[i]); } BoxPointType LocalMap_Points; bool Localmap_Initialized = false; void lasermap_fov_segment() { cub_needrm.clear(); kdtree_delete_counter = 0; kdtree_delete_time = 0.0; V3D pos_LiD = pos_lid; if (!Localmap_Initialized){ for (int i = 0; i < 3; i++){ LocalMap_Points.vertex_min[i] = pos_LiD(i) - cube_len / 2.0; LocalMap_Points.vertex_max[i] = pos_LiD(i) + cube_len / 2.0; } Localmap_Initialized = true; return; } float dist_to_map_edge[3][2]; bool need_move = false; for (int i = 0; i < 3; i++){ dist_to_map_edge[i][0] = fabs(pos_LiD(i) - LocalMap_Points.vertex_min[i]); dist_to_map_edge[i][1] = fabs(pos_LiD(i) - LocalMap_Points.vertex_max[i]); if (dist_to_map_edge[i][0] <= MOV_THRESHOLD * DET_RANGE || dist_to_map_edge[i][1] <= MOV_THRESHOLD * DET_RANGE) need_move = true; } if (!need_move) return; BoxPointType New_LocalMap_Points, tmp_boxpoints; New_LocalMap_Points = LocalMap_Points; float mov_dist = max((cube_len - 2.0 * MOV_THRESHOLD * DET_RANGE) * 0.5 * 0.9, double(DET_RANGE * (MOV_THRESHOLD -1))); for (int i = 0; i < 3; i++){ tmp_boxpoints = LocalMap_Points; if (dist_to_map_edge[i][0] <= MOV_THRESHOLD * DET_RANGE){ New_LocalMap_Points.vertex_max[i] -= mov_dist; New_LocalMap_Points.vertex_min[i] -= mov_dist; tmp_boxpoints.vertex_min[i] = LocalMap_Points.vertex_max[i] - mov_dist; cub_needrm.push_back(tmp_boxpoints); } else if (dist_to_map_edge[i][1] <= MOV_THRESHOLD * DET_RANGE){ New_LocalMap_Points.vertex_max[i] += mov_dist; New_LocalMap_Points.vertex_min[i] += mov_dist; tmp_boxpoints.vertex_max[i] = LocalMap_Points.vertex_min[i] + mov_dist; cub_needrm.push_back(tmp_boxpoints); } } LocalMap_Points = New_LocalMap_Points; points_cache_collect(); double delete_begin = omp_get_wtime(); if(cub_needrm.size() > 0) kdtree_delete_counter = ikdtree.Delete_Point_Boxes(cub_needrm); kdtree_delete_time = omp_get_wtime() - delete_begin; } double mean_preprocess = 0.0; void standard_pcl_cbk(const sensor_msgs::PointCloud2::ConstPtr &msg) { mtx_buffer.lock(); scan_count ++; double preprocess_start_time = omp_get_wtime(); double scan_begin_time = msg->header.stamp.toSec() + lidar_time_offset; if (scan_begin_time < last_timestamp_lidar) { ROS_ERROR("lidar loop back, clear buffer"); lidar_buffer.clear(); } PointCloudXYZI::Ptr ptr(new PointCloudXYZI()); p_pre->process(msg, ptr); lidar_buffer.push_back(ptr); time_buffer.push_back(scan_begin_time); last_timestamp_lidar = scan_begin_time; timestamps_lidar.push_back(last_timestamp_lidar); s_plot11[scan_count] = omp_get_wtime() - preprocess_start_time; mean_preprocess = mean_preprocess * (scan_count - 1) / scan_count + s_plot11[scan_count] / scan_count; mtx_buffer.unlock(); sig_buffer.notify_all(); if (runtime_pos_log) printf("[ pre-process ]: this time: %0.6f ms, mean : %0.6f ms\n", s_plot11[scan_count] * 1000, mean_preprocess * 1000); } double timediff_lidar_wrt_imu = 0.0; bool timediff_set_flg = false; //void livox_pcl_cbk(const livox_ros_driver::CustomMsg::ConstPtr &msg) //{ // mtx_buffer.lock(); // double preprocess_start_time = omp_get_wtime(); // scan_count ++; // if (msg->header.stamp.toSec() < last_timestamp_lidar) // { // ROS_ERROR("lidar loop back, clear buffer"); // lidar_buffer.clear(); // } // last_timestamp_lidar = msg->header.stamp.toSec(); // // if (!time_sync_en && abs(last_timestamp_imu - last_timestamp_lidar) > 10.0 && !imu_buffer.empty() && !lidar_buffer.empty() ) // { // printf("IMU and LiDAR not Synced, IMU time: %lf, lidar header time: %lf \n",last_timestamp_imu, last_timestamp_lidar); // } // // if (time_sync_en && !timediff_set_flg && abs(last_timestamp_lidar - last_timestamp_imu) > 1 && !imu_buffer.empty()) // { // timediff_set_flg = true; // timediff_lidar_wrt_imu = last_timestamp_lidar + 0.1 - last_timestamp_imu; // printf("Self sync IMU and LiDAR, time diff is %.10lf \n", timediff_lidar_wrt_imu); // } // // PointCloudXYZI::Ptr ptr(new PointCloudXYZI()); // p_pre->process(msg, ptr); // lidar_buffer.push_back(ptr); // time_buffer.push_back(last_timestamp_lidar); // // s_plot11[scan_count] = omp_get_wtime() - preprocess_start_time; // mtx_buffer.unlock(); // sig_buffer.notify_all(); //} void imu_cbk(const sensor_msgs::Imu::ConstPtr &msg_in) { publish_count ++; // cout<<"IMU got at: "<header.stamp.toSec()<header.stamp = ros::Time().fromSec(msg_in->header.stamp.toSec() - time_diff_lidar_to_imu); if (abs(timediff_lidar_wrt_imu) > 0.1 && time_sync_en) { msg->header.stamp = \ ros::Time().fromSec(timediff_lidar_wrt_imu + msg_in->header.stamp.toSec()); } double timestamp = msg->header.stamp.toSec(); mtx_buffer.lock(); if (timestamp < last_timestamp_imu) { ROS_WARN("imu loop back, clear buffer"); imu_buffer.clear(); } last_timestamp_imu = timestamp; //update imu time imu_buffer.push_back(msg); mtx_buffer.unlock(); sig_buffer.notify_all(); } double lidar_mean_scantime = 0.0; int scan_num = 0; bool sync_packages(MeasureGroup &meas) { if (lidar_buffer.empty() || imu_buffer.empty()) { return false; } /*** push a lidar scan ***/ if(!lidar_pushed) { meas.lidar = lidar_buffer.front(); meas.lidar_beg_time = time_buffer.front(); if (meas.lidar->points.size() <= 1) // time too little { lidar_end_time = meas.lidar_beg_time + lidar_mean_scantime; ROS_WARN("Too few input point cloud!\n"); } else if (meas.lidar->points.back().curvature / double(1000) < 0.5 * lidar_mean_scantime) { lidar_end_time = meas.lidar_beg_time + lidar_mean_scantime; } else { scan_num ++; lidar_end_time = meas.lidar_beg_time + meas.lidar->points.back().curvature / double(1000); lidar_mean_scantime += (meas.lidar->points.back().curvature / double(1000) - lidar_mean_scantime) / scan_num; } meas.lidar_end_time = lidar_end_time; lidar_pushed = true; } if (last_timestamp_imu < lidar_end_time) { return false; } /*** push imu data, and pop from imu buffer ***/ double imu_time = imu_buffer.front()->header.stamp.toSec(); meas.imu.clear(); while ((!imu_buffer.empty()) && (imu_time < lidar_end_time)) { imu_time = imu_buffer.front()->header.stamp.toSec(); if(imu_time > lidar_end_time) break; meas.imu.push_back(imu_buffer.front()); imu_buffer.pop_front(); } lidar_buffer.pop_front(); time_buffer.pop_front(); lidar_pushed = false; return true; } bool same_point(PointType a, PointType b){ return (fabs(a.x-b.x) < EPSS && fabs(a.y-b.y) < EPSS && fabs(a.z-b.z) < EPSS ); } int process_increments = 0; void map_incremental() { ROS_INFO("[incremental] start"); PointVector PointToAdd; PointVector PointNoNeedDownsample; PointToAdd.reserve(feats_down_size); PointNoNeedDownsample.reserve(feats_down_size); vector::KD_TREE_NODE*, V3F>> roots_need_update_normal; for (int i = 0; i < feats_down_size; i++) { /* transform to world frame */ pointNormalBodyToWorld(&(feats_down_body->points[i]), &(feats_down_world->points[i])); PointType & point_world = feats_down_world->points[i]; ikdtree.updateVoxelInfo(point_world); /* decide if need add to map */ if (!Nearest_Points[i].empty() && flg_EKF_inited) { const PointVector &points_near = Nearest_Points[i]; bool need_add = true; BoxPointType Box_of_Point; PointType downsample_result, mid_point; mid_point.x = floor(point_world.x / filter_size_map_min)*filter_size_map_min + half_map_size; mid_point.y = floor(point_world.y / filter_size_map_min)*filter_size_map_min + half_map_size; mid_point.z = floor(point_world.z / filter_size_map_min)*filter_size_map_min + half_map_size; float dist = calc_dist(point_world, mid_point);//当前点与box中心的距离 if (fabs(points_near[0].x - mid_point.x) > half_map_size && fabs(points_near[0].y - mid_point.y) > half_map_size && fabs(points_near[0].z - mid_point.z) > half_map_size){ // initPointIncInfo(point_world); PointNoNeedDownsample.push_back(point_world); continue; } for (int readd_i = 0; readd_i < NUM_MATCH_POINTS; readd_i ++) { if (points_near.size() < NUM_MATCH_POINTS) break; if (calc_dist(points_near[readd_i], mid_point) < dist) { need_add = false; break; } } if (need_add) { PointToAdd.push_back(point_world); } } else { // initPointIncInfo(point_world); PointToAdd.push_back(point_world); } } ROS_INFO("[incremental]"); double st_time = omp_get_wtime(); add_point_size = ikdtree.Add_Points(PointToAdd, true); ikdtree.Add_Points(PointNoNeedDownsample, false); add_point_size = PointToAdd.size() + PointNoNeedDownsample.size(); kdtree_incremental_time = omp_get_wtime() - st_time; ROS_INFO("kdtree_incremental_time %f", kdtree_incremental_time); } const bool var_contrast(pointWithCov &x, pointWithCov &y) { return (x.cov.diagonal().norm() < y.cov.diagonal().norm()); }; PointCloudXYZI::Ptr pcl_wait_pub(new PointCloudXYZI(500000, 1)); PointCloudXYZI::Ptr pcl_wait_save(new PointCloudXYZI()); void publish_frame_world(const ros::Publisher & pubLaserCloudFull) { if(scan_pub_en) { PointCloudXYZI::Ptr laserCloudFullRes(dense_pub_en ? feats_undistort : feats_down_body); int size = laserCloudFullRes->points.size(); PointCloudXYZI::Ptr laserCloudWorld( \ new PointCloudXYZI(size, 1)); for (int i = 0; i < size; i++) { RGBpointBodyToWorld(&laserCloudFullRes->points[i], \ &laserCloudWorld->points[i]); } sensor_msgs::PointCloud2 laserCloudmsg; pcl::toROSMsg(*laserCloudWorld, laserCloudmsg); laserCloudmsg.header.stamp = ros::Time().fromSec(lidar_end_time); laserCloudmsg.header.frame_id = "camera_init"; pubLaserCloudFull.publish(laserCloudmsg); publish_count -= PUBFRAME_PERIOD; } /**************** save map ****************/ /* 1. make sure you have enough memories /* 2. noted that pcd save will influence the real-time performences **/ if (pcd_save_en) { int size = feats_undistort->points.size(); PointCloudXYZI::Ptr laserCloudWorld( \ new PointCloudXYZI(size, 1)); for (int i = 0; i < size; i++) { RGBpointBodyToWorld(&feats_undistort->points[i], \ &laserCloudWorld->points[i]); } *pcl_wait_save += *laserCloudWorld; static int scan_wait_num = 0; scan_wait_num ++; if (pcl_wait_save->size() > 0 && pcd_save_interval > 0 && scan_wait_num >= pcd_save_interval) { pcd_index ++; string all_points_dir(string(string(ROOT_DIR) + "PCD/scans_") + to_string(pcd_index) + string(".pcd")); pcl::PCDWriter pcd_writer; cout << "current scan saved to /PCD/" << all_points_dir << endl; pcd_writer.writeBinary(all_points_dir, *pcl_wait_save); pcl_wait_save->clear(); scan_wait_num = 0; } } } void publish_frame_body(const ros::Publisher & pubLaserCloudFull_body) { int size = feats_undistort->points.size(); PointCloudXYZI::Ptr laserCloudIMUBody(new PointCloudXYZI(size, 1)); for (int i = 0; i < size; i++) { RGBpointBodyLidarToIMU(&feats_undistort->points[i], \ &laserCloudIMUBody->points[i]); } sensor_msgs::PointCloud2 laserCloudmsg; pcl::toROSMsg(*laserCloudIMUBody, laserCloudmsg); laserCloudmsg.header.stamp = ros::Time().fromSec(lidar_end_time); laserCloudmsg.header.frame_id = "body"; pubLaserCloudFull_body.publish(laserCloudmsg); publish_count -= PUBFRAME_PERIOD; } void publish_effect_world(const ros::Publisher & pubLaserCloudEffect) { PointCloudXYZI::Ptr laserCloudWorld( \ new PointCloudXYZI(effct_feat_num, 1)); for (int i = 0; i < effct_feat_num; i++) { RGBpointBodyToWorld(&laserCloudOri->points[i], \ &laserCloudWorld->points[i]); } sensor_msgs::PointCloud2 laserCloudFullRes3; pcl::toROSMsg(*laserCloudWorld, laserCloudFullRes3); laserCloudFullRes3.header.stamp = ros::Time().fromSec(lidar_end_time); laserCloudFullRes3.header.frame_id = "camera_init"; pubLaserCloudEffect.publish(laserCloudFullRes3); } void publish_map(const ros::Publisher & pubLaserCloudMap) { sensor_msgs::PointCloud2 laserCloudMap; pcl::toROSMsg(*featsFromMap, laserCloudMap); laserCloudMap.header.stamp = ros::Time().fromSec(lidar_end_time); laserCloudMap.header.frame_id = "camera_init"; pubLaserCloudMap.publish(laserCloudMap); } template void set_posestamp(T & out) { out.pose.position.x = state_point.pos(0); out.pose.position.y = state_point.pos(1); out.pose.position.z = state_point.pos(2); out.pose.orientation.x = geoQuat.x; out.pose.orientation.y = geoQuat.y; out.pose.orientation.z = geoQuat.z; out.pose.orientation.w = geoQuat.w; } void publish_odometry(const ros::Publisher & pubOdomAftMapped) { odomAftMapped.header.frame_id = "camera_init"; odomAftMapped.child_frame_id = "body"; odomAftMapped.header.stamp = ros::Time().fromSec(lidar_end_time);// ros::Time().fromSec(lidar_end_time); set_posestamp(odomAftMapped.pose); pubOdomAftMapped.publish(odomAftMapped); auto P = kf.get_P(); for (int i = 0; i < 6; i ++) { int k = i < 3 ? i + 3 : i - 3; odomAftMapped.pose.covariance[i*6 + 0] = P(k, 3); odomAftMapped.pose.covariance[i*6 + 1] = P(k, 4); odomAftMapped.pose.covariance[i*6 + 2] = P(k, 5); odomAftMapped.pose.covariance[i*6 + 3] = P(k, 0); odomAftMapped.pose.covariance[i*6 + 4] = P(k, 1); odomAftMapped.pose.covariance[i*6 + 5] = P(k, 2); } static tf::TransformBroadcaster br; tf::Transform transform; tf::Quaternion q; transform.setOrigin(tf::Vector3(odomAftMapped.pose.pose.position.x, \ odomAftMapped.pose.pose.position.y, \ odomAftMapped.pose.pose.position.z)); q.setW(odomAftMapped.pose.pose.orientation.w); q.setX(odomAftMapped.pose.pose.orientation.x); q.setY(odomAftMapped.pose.pose.orientation.y); q.setZ(odomAftMapped.pose.pose.orientation.z); transform.setRotation( q ); br.sendTransform( tf::StampedTransform( transform, odomAftMapped.header.stamp, "camera_init", "body" ) ); } void publish_path(const ros::Publisher pubPath) { set_posestamp(msg_body_pose); msg_body_pose.header.stamp = ros::Time().fromSec(lidar_end_time); msg_body_pose.header.frame_id = "camera_init"; /*** if path is too large, the rvis will crash ***/ static int jjj = 0; jjj++; if (jjj % 10 == 0) { path.poses.push_back(msg_body_pose); pubPath.publish(path); } if (!have_keyframe) { // V3D euler_body = SO3ToEuler(state_point.rot); // body_keypose_last << euler_body(0), euler_body(1), euler_body(2), state_point.pos(0), state_point.pos(1), state_point.pos(2); state_body_last = state_point; vect3 pos_target; pos_target = state_point.pos + state_point.rot * gt_T_wrt_IMU; msg_target_pose.header.stamp = ros::Time().fromSec(lidar_end_time); msg_target_pose.header.frame_id = "camera_init"; msg_target_pose.pose.position.x = pos_target(0); msg_target_pose.pose.position.y = pos_target(1); msg_target_pose.pose.position.z = pos_target(2); Eigen::Quaterniond quat_target(state_point.rot * gt_R_wrt_IMU); msg_target_pose.pose.orientation.x = quat_target.x(); msg_target_pose.pose.orientation.y = quat_target.y(); msg_target_pose.pose.orientation.z = quat_target.z(); msg_target_pose.pose.orientation.w = quat_target.w(); path_target.poses.push_back(msg_target_pose); have_keyframe = true; return; } Eigen::Quaterniond q_last(state_body_last.rot.coeffs()[3], state_body_last.rot.coeffs()[0], state_body_last.rot.coeffs()[1], state_body_last.rot.coeffs()[2]); Eigen::Quaterniond q_curr(state_point.rot.coeffs()[3], state_point.rot.coeffs()[0], state_point.rot.coeffs()[1], state_point.rot.coeffs()[2]); Eigen::Matrix3d r_incre(q_last * q_curr.inverse()); Eigen::Vector3d rpy = r_incre.eulerAngles(2, 1, 0); float x, y, z, roll, pitch, yaw; x = state_body_last.pos(0) - state_point.pos(0); y = state_body_last.pos(1) - state_point.pos(1); z = state_body_last.pos(2) - state_point.pos(2); roll = rpy(2); pitch = rpy(1); yaw = rpy(0); float surroundingkeyframeAddingAngleThreshold = 0.2; float surroundingkeyframeAddingDistThreshold = 1.0; if (abs(roll) >= surroundingkeyframeAddingAngleThreshold || abs(pitch) >= surroundingkeyframeAddingAngleThreshold || abs(yaw) >= surroundingkeyframeAddingAngleThreshold || sqrt(x*x + y*y + z*z) >= surroundingkeyframeAddingDistThreshold) // if (jjj % 20 == 0) { vect3 pos_target; pos_target = state_point.pos + state_point.rot * gt_T_wrt_IMU; Eigen::Quaterniond quat_target(state_point.rot * gt_R_wrt_IMU); // Eigen::Quaterniond quat_target(T.block<3, 3>(0, 0)); msg_target_pose.header.stamp = ros::Time().fromSec(lidar_end_time); msg_target_pose.header.frame_id = "camera_init"; msg_target_pose.pose.position.x = pos_target(0); msg_target_pose.pose.position.y = pos_target(1); msg_target_pose.pose.position.z = pos_target(2); msg_target_pose.pose.orientation.x = quat_target.x(); msg_target_pose.pose.orientation.y = quat_target.y(); msg_target_pose.pose.orientation.z = quat_target.z(); msg_target_pose.pose.orientation.w = quat_target.w(); path_target_end.poses.push_back(msg_target_pose); } } void h_share_model(state_ikfom &s, esekfom::dyn_share_datastruct &ekfom_data) { // ROS_INFO("[h_share_model]"); double match_start = omp_get_wtime(); laserCloudOri->clear(); corr_normvect->clear(); total_residual = 0.0; V3F body(s.pos.x(), s.pos.y(), s.pos.z()); int num_same_nearest = 0, num_neighbor_nearest = 0, num_diff_nearest = 0; int num_node_faraway = 0; double time_tree_search = 0, time_voxel_search = 0, time_cov2w = 0; int num_valid_voxel_neighbors = 0; /** closest surface search and residual computation **/ #ifdef MP_EN omp_set_num_threads(MP_PROC_NUM); #pragma omp parallel for #endif for (int i = 0; i < feats_down_size; i++) { /* transform to world frame */ pointNormalBodyToWorld(&(feats_down_body->points[i]), &(feats_down_world->points[i]));//point: world <-- imu <-- lidar const PointType &point_body = feats_down_body->points[i]; const PointType &point_world = feats_down_world->points[i]; V3F body2point = (point_world.getVector3fMap() - body).normalized(); //normalized vector vector pointSearchSqDis(NUM_MATCH_POINTS); auto &points_near = Nearest_Points[i]; auto &voxels_near = Nearest_Nodes[i]; float angle = 60.0 / 180.0 * M_PI; // VF(4) pabcd_surfel; // plane: n * p + d = 0 // bool surfel_valid = false; // bool voxel_search_valid = false; // bool tree_search_valid = false; if (ekfom_data.converge) { /** Find the closest surfaces in the map **/ // PointVector voxel_Nearest_Points; // vector::KD_TREE_NODE*> voxel_Nearest_Nodes; bool voxel_searching_valid = false; // ikdtree.Voxel_Search(point_world, NUM_MATCH_POINTS, voxel_Nearest_Points, voxel_Nearest_Nodes); // if (voxel_hash_en) // { // TicToc t_voxel; // //todo fix bugs // ikdtree.Voxel_Search_test(point_world, NUM_MATCH_POINTS, points_near, pointSearchSqDis, voxels_near); // time_voxel_search += t_voxel.toc(); //// ROS_INFO("#%d hash done", i); // //// // point to voxels association (surfel association) //// VF(4) pabcd_surfel; // plane: n * p + d = 0 //// bool surfel_valid = mergeVoxelCheck(voxels_near, body2point, point_world, pabcd_surfel, 0.15f); //// if (!surfel_valid) //// surfel_valid = singleVoxelCheck(voxels_near, body2point, point_world, pabcd_surfel, 0.2f); //// /// !!! pabcd_surfel(3) record point to surfel distance // //// voxel_searching_valid = surfel_valid; // if (voxels_near.size() < NUM_MATCH_POINTS) { // voxel_searching_valid = false; // } // else { // voxel_searching_valid = true; // ++num_valid_voxel_neighbors; // } // } if (!voxel_searching_valid) { TicToc t_tree; ikdtree.Nearest_Search(point_world, NUM_MATCH_POINTS, points_near, pointSearchSqDis, voxels_near); time_tree_search += t_tree.toc(); } bool planeValid = true; if (points_near.size() < NUM_MATCH_POINTS || pointSearchSqDis[NUM_MATCH_POINTS - 1] > 5) planeValid = false; if (planeValid && check_normal) //check normal direction { float sum_diff_angle = 0; // angle between normals int num_fail_normal = 0; for (int j = 0; j < NUM_MATCH_POINTS; j++) { //check map points normal direction, to ensure lidar can see it if (!voxels_near[j]) continue; Eigen::Vector3f voxel_normal = voxels_near[j]->normal; if (voxel_normal.dot(body2point) > 0) { // point_selected_surf[i] = false; // planeValid = false; ++num_fail_normal; continue; } sum_diff_angle += acos(point_world.getNormalVector3fMap().dot(voxel_normal)); } // point normal and map surface normal are not consistent if (num_fail_normal >= NUM_MATCH_POINTS - 1 || sum_diff_angle / (float)(NUM_MATCH_POINTS - num_fail_normal) > angle ) // if (num_fail_normal >= NUM_MATCH_POINTS - 1) planeValid = false; } point_selected_surf[i] = planeValid; } if (!point_selected_surf[i]) continue; point_selected_surf[i] = false; VF(4) pabcd; //[n, d], n * p + d = 0 bool good_plane_feature = false; TicToc t_pca; VF(4) pabcd_surfel; bool good_surfel_feature = false; float p2surfel = 10000; V3F centroid; if (cloud_surfel) good_surfel_feature = cloudSurfel(voxels_near, pabcd_surfel, centroid, 0.15f); if (good_surfel_feature) { // PointType &point_nearest = nodes_near[0]->point; V3F cen2point(point_world.x - centroid(0), point_world.y - centroid(1), point_world.z -centroid(2)); p2surfel = pabcd_surfel.head(3).dot(cen2point); num_cloud_surfel++; } if (!good_surfel_feature && point_surfel) { //todo maybe not the nearest voxel, compute point to surfel probability, see VoxelMap if (voxels_near[0]) { PointType &point_nearest = points_near[0]; bool same_voxel = abs(point_nearest.x - point_world.x) <= half_map_size && abs(point_nearest.y - point_world.y) <= half_map_size && abs(point_nearest.z - point_world.z) <= half_map_size; const auto &voxel_info = voxels_near[0]; // point and nearest neighbor inside the same voxel // todo 3 * standard deviation if (same_voxel && (voxel_info->num_points < 0 || voxel_info->num_points > surfel_points_min)) { V3F plane_centroid(voxel_info->mean_x, voxel_info->mean_y, voxel_info->mean_z); V3F cen2point(point_world.x - plane_centroid(0), point_world.y - plane_centroid(1), point_world.z - plane_centroid(2)); p2surfel = voxel_info->normal.dot(cen2point); // V3F cen2near(point_nearest.x - plane_centroid(0), point_nearest.y - plane_centroid(1), // point_nearest.z - plane_centroid(2)); if (fabs(p2surfel) < 0.20f) { good_surfel_feature = true; pabcd_surfel.head(3) = voxel_info->normal; pabcd_surfel(3) = p2surfel; ++num_point_surfel; } } } } // TicToc t_esti_plane; if (good_surfel_feature) { pabcd = pabcd_surfel; good_plane_feature = true; } else good_plane_feature = esti_plane(pabcd, points_near, 0.1f); if (good_plane_feature) { //plane distance V3F p_body = point_body.getVector3fMap(); float pd2 = pabcd(0) * point_world.x + pabcd(1) * point_world.y + pabcd(2) * point_world.z + pabcd(3); float s = 1 - 0.9 * fabs(pd2) / sqrt(p_body.norm()); if (good_surfel_feature) { // std::cout<< "surfel normal :\n" <points[i]; V3D point_this_be(laser_p.x, laser_p.y, laser_p.z); M3D point_be_crossmat; point_be_crossmat << SKEW_SYM_MATRX(point_this_be); V3D point_this = s.offset_R_L_I * point_this_be + s.offset_T_L_I; M3D point_crossmat; point_crossmat<points[i]; V3D norm_vec(norm_p.x, norm_p.y, norm_p.z); /*** calculate the Measuremnt Jacobian matrix H ***/ V3D C(s.rot.conjugate() *norm_vec); V3D A(point_crossmat * C); // P(IMU)^ [R(imu <-- w) * normal_w] if (extrinsic_est_en) { // B = lidar_p^ R(L <-- I) * corr_normal_I // B = lidar_p^ R(L <-- I) * R(I <-- W) * normal_W V3D B(point_be_crossmat * s.offset_R_L_I.conjugate() * C); //s.rot.conjugate()*norm_vec); ekfom_data.h_x.block<1, 12>(i,0) << norm_p.x, norm_p.y, norm_p.z, VEC_FROM_ARRAY(A), VEC_FROM_ARRAY(B), VEC_FROM_ARRAY(C); } else { ekfom_data.h_x.block<1, 12>(i,0) << norm_p.x, norm_p.y, norm_p.z, VEC_FROM_ARRAY(A), 0.0, 0.0, 0.0, 0.0, 0.0, 0.0; } /*** Measuremnt: distance to the closest surface/corner ***/ ekfom_data.h(i) = -norm_p.intensity; } solve_time += omp_get_wtime() - solve_start_; } void saveTraj(const string & pose_target_file) { //also interpolate pose at lidar begin time int begin_time_ptr = 0; int begin_time_size = timestamps_lidar.size(); double begin_time = timestamps_lidar[0]; // int end_time_ptr_left = 0; double end_time_left = path_target_end.poses[0].header.stamp.toSec(); while (begin_time_ptr < begin_time_size) { begin_time = timestamps_lidar[begin_time_ptr]; if (begin_time > end_time_left) break; ++begin_time_ptr; } printf("\n..............Saving path................\n"); printf("path file: %s\n", pose_target_file.c_str()); ofstream of_beg(pose_target_file); of_beg.setf(ios::fixed, ios::floatfield); of_beg.precision(12); of_beg<< path_target_end.poses[0].header.stamp.toSec()<< " " <("publish/path_en",path_en, true); nh.param("publish/scan_publish_en",scan_pub_en, true); nh.param("publish/dense_publish_en",dense_pub_en, true); nh.param("publish/scan_bodyframe_pub_en",scan_body_pub_en, true); nh.param("max_iteration",NUM_MAX_ITERATIONS,4); nh.param("map_file_path",map_file_path,""); nh.param("common/lid_topic",lid_topic,"/livox/lidar"); nh.param("common/imu_topic", imu_topic,"/livox/imu"); nh.param("common/time_sync_en", time_sync_en, false); nh.param("common/time_offset_lidar_to_imu", time_diff_lidar_to_imu, 0.0); nh.param("common/lidar_time_offset", lidar_time_offset, 0.0); nh.param("filter_size_corner",filter_size_corner_min,0.5); nh.param("filter_size_surf",filter_size_surf_min,0.5); nh.param("filter_size_map",filter_size_map_min,0.5); nh.param("cube_side_length",cube_len,200); nh.param("mapping/det_range",DET_RANGE,300.f); nh.param("mapping/fov_degree",fov_deg,180); nh.param("mapping/gyr_cov",gyr_cov,0.1); nh.param("mapping/acc_cov",acc_cov,0.1); nh.param("mapping/b_gyr_cov",b_gyr_cov,0.0001); nh.param("mapping/b_acc_cov",b_acc_cov,0.0001); nh.param("preprocess/blind", p_pre->blind, 0.01); nh.param("preprocess/lidar_type", p_pre->lidar_type, AVIA); nh.param("preprocess/scan_line", p_pre->N_SCANS, 16); nh.param("preprocess/scan_line", N_SCAN, 16); nh.param("preprocess/Horizon_SCAN", Horizon_SCAN, 1800); nh.param("preprocess/Horizon_SCAN", p_pre->Horizon_SCAN, 1800); nh.param("preprocess/timestamp_unit", p_pre->time_unit, US); nh.param("preprocess/scan_rate", p_pre->SCAN_RATE, 10); nh.param("point_filter_num", p_pre->point_filter_num, 2); nh.param("feature_extract_enable", p_pre->feature_enabled, false); nh.param("runtime_pos_log_enable", runtime_pos_log, 0); nh.param("mapping/extrinsic_est_en", extrinsic_est_en, true); nh.param("pcd_save/pcd_save_en", pcd_save_en, false); nh.param("pcd_save/save_ikdtree_map", save_ikdtree_map, false); nh.param("pcd_save/save_final_map", save_final_map, false); nh.param("pcd_save/interval", pcd_save_interval, -1); nh.param>("mapping/extrinsic_T", extrinT, vector()); nh.param>("mapping/extrinsic_R", extrinR, vector()); half_map_size = filter_size_map_min * 0.5; // surfel nh.param("surfel/planarity", planarity, 1.0f); nh.param("surfel/mid2min", mid2min, 100.0f); nh.param("surfel/surfel_points_min", surfel_points_min, 20); nh.param("surfel/surfel_points_max", surfel_points_max, 100); nh.param("surfel/angle_threshold", angle_threshold, 10.0); nh.param("surfel/cloud_surfel", cloud_surfel, true); nh.param("surfel/point_surfel", point_surfel, true); if (surfel_points_min < 20) surfel_points_min = 20; if (surfel_points_max < surfel_points_min * 2) surfel_points_max = surfel_points_min * 2; ikdtree.max_voxel_points_size = surfel_points_max; // ring Fals Normal Estimation parameters nh.param("normal/compute_table", p_pre->compute_table, false); nh.param("normal/compute_normal", p_pre->compute_normal, false); nh.param("normal/check_normal", check_normal, true); nh.param("normal/ring_table_dir", ring_table_dir, "/tmp"); std::string PROJECT_NAME = "log_lio"; std::string pkg_path = ros::package::getPath(PROJECT_NAME); ring_table_dir = pkg_path + ring_table_dir; p_pre->ring_table_dir = ring_table_dir; p_pre->runtime_log = runtime_pos_log; cout<<"p_pre->lidar_type "<lidar_type<initNormalEstimator(); //voxel map nh.param>("mapping/layer_point_size", layer_point_size,vector()); for (int i = 0; i < layer_point_size.size(); i++) { layer_size.push_back(layer_point_size[i]); } nh.param("mapping/max_layer", max_layer, 2); nh.param("mapping/max_points_size", max_points_size, 100); nh.param("mapping/max_cov_points_size", max_cov_points_size, 100); nh.param("mapping/voxel_size", max_voxel_size, 1.0); nh.param("mapping/plannar_threshold", min_eigen_value, 0.01); nh.param("mapping/voxel_hash_en", voxel_hash_en, true); ikdtree.layer_point_size = layer_size; ikdtree.max_layer = max_layer; ikdtree.max_points_size = max_points_size; ikdtree.max_cov_points_size = max_cov_points_size; ikdtree.max_voxel_size = max_voxel_size; // ikdtree.half_voxel_size = max_voxel_size * 0.5; ikdtree.min_eigen_value = min_eigen_value; path.header.stamp = ros::Time::now(); path.header.frame_id ="camera_init"; /*** variables definition ***/ int effect_feat_num = 0, frame_num = 0; double deltaT, deltaR, aver_time_consu = 0, aver_time_icp = 0, aver_time_match = 0, aver_time_incre = 0, aver_time_solve = 0, aver_time_const_H_time = 0; bool flg_EKF_converged, EKF_stop_flg = 0; // double aver_time_normal = 0; FOV_DEG = (fov_deg + 10.0) > 179.9 ? 179.9 : (fov_deg + 10.0); HALF_FOV_COS = cos((FOV_DEG) * 0.5 * PI_M / 180.0); _featsArray.reset(new PointCloudXYZI()); memset(point_selected_surf, true, sizeof(point_selected_surf)); memset(res_last, -1000.0f, sizeof(res_last)); downSizeFilterSurf.setLeafSize(filter_size_surf_min, filter_size_surf_min, filter_size_surf_min); downSizeFilterMap.setLeafSize(filter_size_map_min, filter_size_map_min, filter_size_map_min); memset(point_selected_surf, true, sizeof(point_selected_surf));//重复？ memset(res_last, -1000.0f, sizeof(res_last)); Lidar_T_wrt_IMU<set_extrinsic(Lidar_T_wrt_IMU, Lidar_R_wrt_IMU); p_imu->set_gyr_cov(V3D(gyr_cov, gyr_cov, gyr_cov)); p_imu->set_acc_cov(V3D(acc_cov, acc_cov, acc_cov)); p_imu->set_gyr_bias_cov(V3D(b_gyr_cov, b_gyr_cov, b_gyr_cov)); p_imu->set_acc_bias_cov(V3D(b_acc_cov, b_acc_cov, b_acc_cov)); double epsi[23] = {0.001}; fill(epsi, epsi+23, 0.001); kf.init_dyn_share(get_f, df_dx, df_dw, h_share_model, NUM_MAX_ITERATIONS, epsi); /*** debug record ***/ FILE *fp; string pos_log_dir = root_dir + "/Log/pos_log.txt"; fp = fopen(pos_log_dir.c_str(),"w"); ofstream fout_pre, fout_out, fout_dbg; fout_pre.open(DEBUG_FILE_DIR("mat_pre.txt"),ios::out); fout_out.open(DEBUG_FILE_DIR("mat_out.txt"),ios::out); fout_dbg.open(DEBUG_FILE_DIR("dbg.txt"),ios::out); if (fout_pre && fout_out) cout << "~~~~"<>("ground_truth/extrinsic_T", gt_extrinT, vector()); nh.param>("ground_truth/extrinsic_R", gt_extrinR, vector()); gt_T_wrt_IMU<lidar_type == AVIA ? \ // nh.subscribe(lid_topic, 200000, livox_pcl_cbk) : \ // nh.subscribe(lid_topic, 200000, standard_pcl_cbk); ros::Subscriber sub_pcl = nh.subscribe(lid_topic, 200000, standard_pcl_cbk); ros::Subscriber sub_imu = nh.subscribe(imu_topic, 200000, imu_cbk); ros::Publisher pubLaserCloudFull = nh.advertise ("/cloud_registered", 100000); ros::Publisher pubLaserCloudFull_body = nh.advertise ("/cloud_registered_body", 100000); ros::Publisher pubLaserCloudEffect = nh.advertise ("/cloud_effected", 100000); ros::Publisher pubLaserCloudMap = nh.advertise ("/Laser_map", 100000); ros::Publisher pubOdomAftMapped = nh.advertise ("/Odometry", 100000); ros::Publisher pubPath = nh.advertise ("/path", 100000); //------------------------------------------------------------------------------------------------------ signal(SIGINT, SigHandle); ros::Rate rate(5000); bool status = ros::ok(); while (status) { if (flg_exit) break; ros::spinOnce(); if(sync_packages(Measures)) { if (flg_first_scan) { first_lidar_time = Measures.lidar_beg_time; p_imu->first_lidar_time = first_lidar_time; flg_first_scan = false; continue; } double t0,t1,t2,t3,t4,t5,match_start, solve_start, svd_time; match_time = 0; kdtree_search_time = 0.0; solve_time = 0; solve_const_H_time = 0; svd_time = 0; t0 = omp_get_wtime(); p_imu->Process(Measures, kf, feats_undistort); state_point = kf.get_x(); auto state_cov = kf.get_P(); pos_lid = state_point.pos + state_point.rot * state_point.offset_T_L_I; if (feats_undistort->empty() || (feats_undistort == NULL)) { ROS_WARN("No point, skip this scan!\n"); continue; } flg_EKF_inited = (Measures.lidar_beg_time - first_lidar_time) < INIT_TIME ? false : true; /*** Segment the map in lidar FOV ***/ lasermap_fov_segment(); //todo do not downsample the very first scan here but in the tree building /*** downsample the feature points in a scan ***/ downSizeFilterSurf.setInputCloud(feats_undistort); downSizeFilterSurf.filter(*feats_down_body); // cloud in lidar frame for (auto & p : feats_down_body->points) p.getNormalVector3fMap().normalize(); // pcl::io::savePCDFile("/tmp/feats_down_body.pcd", *feats_down_body); ROS_INFO("feats_undistort: %d, feats_down_body: %d", (int)feats_undistort->size(), (int)feats_down_body->size()); t1 = omp_get_wtime(); feats_down_size = feats_down_body->points.size(); /*** initialize the map kdtree ***/ if(ikdtree.Root_Node == nullptr) { double t_init_map = omp_get_wtime(); if(feats_down_size > 5) { ikdtree.set_downsample_param(filter_size_map_min); feats_down_world->resize(feats_down_size); for(int i = 0; i < feats_down_size; i++) { pointNormalBodyToWorld(&(feats_down_body->points[i]), &(feats_down_world->points[i]));//point: world <-- imu <-- lidar } ikdtree.Build(feats_down_world->points); } continue; } /*** ICP and iterated Kalman filter update ***/ if (feats_down_size < 5) { ROS_WARN("No point, skip this scan!\n"); continue; } normvec->resize(feats_down_size); feats_down_world->resize(feats_down_size); // lidar --> imu V3D ext_euler = SO3ToEuler(state_point.offset_R_L_I); fout_pre<clear(); featsFromMap->points = ikdtree.PCL_Storage; featsFromMap->resize(featsFromMap->points.size()); pcl::io::savePCDFile("/tmp/ikdtree_map.pcd", *featsFromMap); } } pointSearchInd_surf.resize(feats_down_size); Nearest_Points.resize(feats_down_size); Nearest_Nodes.resize(feats_down_size); int rematch_num = 0; bool nearest_search_en = true; // t2 = omp_get_wtime(); /*** iterated state estimation ***/ double t_update_start = omp_get_wtime(); double solve_H_time = 0; num_cloud_surfel = 0; num_point_surfel = 0; kf.update_iterated_dyn_share_modified(LASER_POINT_COV, solve_H_time); ROS_INFO("[association]: cloud surfel: %d, point surfel: %d", num_cloud_surfel / NUM_MAX_ITERATIONS, num_point_surfel / NUM_MAX_ITERATIONS); state_point = kf.get_x(); euler_cur = SO3ToEuler(state_point.rot); pos_lid = state_point.pos + state_point.rot * state_point.offset_T_L_I; geoQuat.x = state_point.rot.coeffs()[0]; geoQuat.y = state_point.rot.coeffs()[1]; geoQuat.z = state_point.rot.coeffs()[2]; geoQuat.w = state_point.rot.coeffs()[3]; double t_update_end = omp_get_wtime(); /******* Publish odometry *******/ publish_odometry(pubOdomAftMapped); /*** add the feature points to map kdtree ***/ t3 = omp_get_wtime(); map_incremental(); t5 = omp_get_wtime(); ROS_INFO("map_incremental %d surfels: %d", ikdtree.validnum(), num_surfel); // pcl::io::savePCDFile("/tmp/feats_down_world.pcd", *feats_down_world); // /*** add the points to the voxel map ***/ // voxel_map_incremental(); /******* Publish points *******/ if (path_en) publish_path(pubPath); if (scan_pub_en || pcd_save_en) publish_frame_world(pubLaserCloudFull); if (scan_pub_en && scan_body_pub_en) publish_frame_body(pubLaserCloudFull_body); // publish_effect_world(pubLaserCloudEffect); // publish_map(pubLaserCloudMap); /*** Debug variables ***/ if (runtime_pos_log) { ROS_INFO("feats_undistort: %d, feats_down_body: %d", (int)feats_undistort->size(), (int)feats_down_body->size()); ROS_INFO("[association]: cloud sufel: %d, point surfel: %d", num_cloud_surfel / NUM_MAX_ITERATIONS, num_point_surfel / NUM_MAX_ITERATIONS); ROS_INFO("[map_incremental] tree valid: %d surfels: %d", ikdtree.validnum(), num_surfel); frame_num ++; kdtree_size_end = ikdtree.size(); aver_time_consu = aver_time_consu * (frame_num - 1) / frame_num + (t5 - t0) / frame_num; aver_time_icp = aver_time_icp * (frame_num - 1)/frame_num + (t_update_end - t_update_start) / frame_num; aver_time_match = aver_time_match * (frame_num - 1)/frame_num + (match_time)/frame_num; aver_time_incre = aver_time_incre * (frame_num - 1)/frame_num + (kdtree_incremental_time)/frame_num; aver_time_solve = aver_time_solve * (frame_num - 1)/frame_num + (solve_time + solve_H_time)/frame_num; aver_time_const_H_time = aver_time_const_H_time * (frame_num - 1)/frame_num + solve_time / frame_num; // aver_time_normal = aver_time_normal * (frame_num - 1)/frame_num + t_normal / frame_num; T1[time_log_counter] = Measures.lidar_beg_time; s_plot[time_log_counter] = t5 - t0; s_plot2[time_log_counter] = feats_undistort->points.size(); s_plot3[time_log_counter] = kdtree_incremental_time; s_plot4[time_log_counter] = kdtree_search_time; s_plot5[time_log_counter] = kdtree_delete_counter; s_plot6[time_log_counter] = kdtree_delete_time; s_plot7[time_log_counter] = kdtree_size_st; s_plot8[time_log_counter] = kdtree_size_end; s_plot9[time_log_counter] = aver_time_consu; s_plot10[time_log_counter] = add_point_size; time_log_counter ++; printf("[ mapping ]: time: IMU + Map + Cov + Input Downsample: %0.6f this ICP: %0.6f this map incre: %0.6f ave match: %0.6f ave solve: %0.6f ave total: %0.6f ave icp: %0.6f ave construct H: %0.6f\n", t1-t0, t3-t1, t5-t3, aver_time_match, aver_time_solve, aver_time_consu, aver_time_icp, aver_time_const_H_time); ext_euler = SO3ToEuler(state_point.offset_R_L_I); fout_out << setw(20) << Measures.lidar_beg_time - first_lidar_time << " " << euler_cur.transpose() << " " << state_point.pos.transpose()<< " " << ext_euler.transpose() << " "<points.size()<size() > 0 && pcd_save_en) { string file_name = string("scans.pcd"); string all_points_dir(string(string(ROOT_DIR) + "PCD/") + file_name); pcl::PCDWriter pcd_writer; cout << "current scan saved to /PCD/" << file_name< t, s_vec, s_vec2, s_vec3, s_vec4, s_vec5, s_vec6, s_vec7; FILE *fp2; string log_dir = root_dir + "/Log/fast_lio_time_log.csv"; fp2 = fopen(log_dir.c_str(),"w"); fprintf(fp2,"time_stamp, total time, scan point size, incremental time, search time, delete size, delete time, tree size st, tree size end, add point size, preprocess time\n"); for (int i = 0;iclear(); featsFromMap->points = ikdtree.PCL_Storage; featsFromMap->resize(featsFromMap->points.size()); pcl::io::savePCDFile("/tmp/ikdtree_final_map.pcd", *featsFromMap); } // not test yet if (p_pre->compute_table) { TicToc t_m; printf(".....Computing M inverse....\n"); p_pre->range_image.computeMInverse(); printf("Computing M inverse cost: %fms\n", t_m.toc()); printf(".....Saving range image lookup table....\n"); p_pre->range_image.saveLookupTable(p_pre->ring_table_dir, "ring" + std::to_string(N_SCAN)); } printf(".....[ikdtree] voxel2node: %d, voxel2info: %d\n", (int)ikdtree.voxel2node.size(), (int)ikdtree.voxel2info.size()); printf(".....check corresponding....\n"); ikdtree.checkCorrespondding(ikdtree.Root_Node); printf("nodes %d\n", ikdtree.num_nodes); printf("node --> voxel null %d --> info null %d\n", ikdtree.num_node2_voxel_null, ikdtree.voxel2info_null); printf("node --> voxel valid %d --> info valid %d\n", ikdtree.num_node2voxel_valid, ikdtree.voxel2info_valid); printf("node --> info same %d diff %d\n", ikdtree.num_same_info, ikdtree.num_diff_info); printf("node point --> voxel same %d\n", ikdtree.num_same_voxel_by_pointAndnode); int num_no_points = 0, num_fixed_voxel = 0, num_active_voxel = 0; int num_voxels = 0; double mean_active_points = 0; unsigned int sum_active_points = 0; for (auto a : ikdtree.voxel2info) { if (a.second->num_points < 0 || a.second->num_points >= surfel_points_max) ++num_fixed_voxel; else if (a.second->num_points == 0) ++num_no_points; else { ++num_active_voxel; sum_active_points += a.second->num_points; mean_active_points = mean_active_points * (double)(num_active_voxel - 1) / (double)(num_active_voxel) + (double)a.second->num_points / (double)num_active_voxel; } ++num_voxels; } printf("num_no_points %d num_fixed_voxel %d num_active_voxel %d active points sum: %d mean: %f\n", num_no_points, num_fixed_voxel, num_active_voxel, sum_active_points, mean_active_points); return 0; } ================================================ FILE: src/preprocess.cpp ================================================ #include "preprocess.h" #include "point_type.h" #include "tic_toc.h" #define RETURN0 0x00 #define RETURN0AND1 0x10 Preprocess::Preprocess() :feature_enabled(0), lidar_type(AVIA), blind(0.01), point_filter_num(1) { inf_bound = 10; N_SCANS = 6; SCAN_RATE = 10; group_size = 8; disA = 0.01; disA = 0.1; // B? p2l_ratio = 225; limit_maxmid =6.25; limit_midmin =6.25; limit_maxmin = 3.24; jump_up_limit = 170.0; jump_down_limit = 8.0; cos160 = 160.0; edgea = 2; edgeb = 0.1; smallp_intersect = 172.5; smallp_ratio = 1.2; given_offset_time = false; jump_up_limit = cos(jump_up_limit/180*M_PI); jump_down_limit = cos(jump_down_limit/180*M_PI); cos160 = cos(cos160/180*M_PI); smallp_intersect = cos(smallp_intersect/180*M_PI); } Preprocess::~Preprocess() {} void Preprocess::set(bool feat_en, int lid_type, double bld, int pfilt_num) { feature_enabled = feat_en; lidar_type = lid_type; blind = bld; point_filter_num = pfilt_num; } //void Preprocess::process(const livox_ros_driver::CustomMsg::ConstPtr &msg, PointCloudXYZI::Ptr &pcl_out) //{ // avia_handler(msg); // *pcl_out = pl_surf; //} void Preprocess::saveNormalPCD(const std::string& file_name, const pcl::PointCloud::Ptr& cloud, const cv::Mat& normals_mat) { ROS_ASSERT(rangeMat.size == normals_mat.size); pcl::PointCloud::Ptr cloud_normal(new pcl::PointCloud); for (int i = 0; i < normals_mat.rows; ++i) { for (int j = 0; j < normals_mat.cols; ++j) { if (rangeMat.at(i, j) == FLT_MAX) //如果该珊格已经有点，则珊格内不再加入后续的点 continue; //// int index = j + i * normals_mat.cols; //点在深度图像中的索引 const int index = image2cloud[j + i * image_cols]; const PointXYZIRT& thisPoint = cloud->points[index]; // int index = j + i * normals_mat.cols; //点在深度图像中的索引 // const PointType& thisPoint = fullCloud->points[index]; pcl::PointXYZINormal pn; pn.x = thisPoint.x; pn.y = thisPoint.y; pn.z = thisPoint.z; const cv::Vec3f & n_cv = normals_mat.at(i, j); pn.normal_x = n_cv(0); pn.normal_y = n_cv(1); pn.normal_z = n_cv(2); cloud_normal->push_back(pn); } } // if (cloud_normal->size() > 0) // pcl::io::savePCDFile(file_name, *cloud_normal); } void Preprocess::projectPointCloud(const pcl::PointCloud::Ptr& cloud) { // if (!compute_table) rangeMat = cv::Mat(N_SCANS, image_cols, CV_32F, cv::Scalar::all(FLT_MAX)); image2cloud.clear(); image2cloud.resize(N_SCANS * image_cols, -1); TicToc t_range_image; int cloudSize = (int)cloud->points.size(); cloud2image.clear(); cloud2image.resize(cloudSize, -1); // range image projection for (int i = 0; i < cloudSize; ++i) { const PointXYZIRT& thisPoint = cloud->points[i]; int rowIdn, columnIdn; if (!pointInImage(thisPoint, rowIdn, columnIdn)) continue; float range = sqrt(thisPoint.x * thisPoint.x + thisPoint.y * thisPoint.y + thisPoint.z * thisPoint.z);// sqrt(x^2 + y^2 + z^2) rangeMat.at(rowIdn, columnIdn) = range; //record range after correcting int index = columnIdn + rowIdn * image_cols; image2cloud[index] = i; cloud2image[i] = index; } } void Preprocess::computeRingNormals(const pcl::PointCloud::Ptr& cloud) { // not test yet if (compute_table) { TicToc t_range_image; range_image.buildTableFromRings(cloud); ROS_WARN("build range image from rings cost: %fms", t_range_image.toc()); return; } TicToc t_normal; range_image.computeNormals(rangeMat, normals, plane_residual); ///output: normalized normals } void Preprocess::flipNormalsTowardCenterAndNormalized(const cv::Vec3f& center, const pcl::PointCloud::Ptr& cloud, cv::Mat& image_normals) { ROS_ASSERT(rangeMat.size == image_normals.size); for (int i = 0; i < N_SCANS; ++i)//row for (int j = 0; j < image_cols; ++j) if (rangeMat.at(i, j) != FLT_MAX) { const int point_id = image2cloud[j + i * image_cols]; const PointXYZIRT &thisPoint = cloud->points[point_id]; // vector: from center to point cv::Vec3f vc2p(thisPoint.x - center(0), thisPoint.y - center(1), thisPoint.z - center(2)); vc2p /= norm(vc2p); cv::Vec3f &n = image_normals.at(i, j); ///already normalized if (vc2p.dot(n) > 0) n *= -1; } } void Preprocess::NormalizeNormals(cv::Mat& image_normals) { ROS_ASSERT(rangeMat.size == image_normals.size); for (int i = 0; i < N_SCANS; ++i) for (int j = 0; j < image_cols; ++j) if (rangeMat.at(i, j) != FLT_MAX) { cv::Vec3f &n = image_normals.at(i, j); n /= norm(n); } } void Preprocess::extractCloudAndNormals(const pcl::PointCloud::Ptr& cloud) { TicToc t_flip; cv::Vec3f lidar(0, 0, 0); flipNormalsTowardCenterAndNormalized(lidar, cloud, normals); // smooth normals after flipping TicToc t_smo; cv::medianBlur(normals, normals, 5); NormalizeNormals(normals); return; cloud_with_normal.reset(new PointCloudXYZI()); cloud_with_normal->resize(cloud->size()); int p = 0; TicToc t_e; int num_filtered = 0; int count = 0; // extract segmented cloud for lidar odometry for (int i = 0; i < N_SCANS; ++i) { for (int j = 0; j < image_cols; ++j) { if (rangeMat.at(i,j) != FLT_MAX)//深度图内有信息 { ++count; if (count % point_filter_num != 0) continue; // save extracted cloud const int point_id = image2cloud[j + i * image_cols]; const cv::Vec3f & n_cv = normals.at(i, j); cloud_with_normal->points[point_id].normal_x = n_cv(0); cloud_with_normal->points[point_id].normal_y = n_cv(1); cloud_with_normal->points[point_id].normal_z = n_cv(2); } } } } void Preprocess::process(const sensor_msgs::PointCloud2::ConstPtr &msg, PointCloudXYZI::Ptr &pcl_out) { switch (time_unit) { case SEC: time_unit_scale = 1.e3f; break; case MS: time_unit_scale = 1.f; break; case US: time_unit_scale = 1.e-3f; break; case NS: time_unit_scale = 1.e-6f; break; default: time_unit_scale = 1.f; break; } switch (lidar_type) { case OUSTER: ouster_handler(msg); break; case VELODYNE: velodyne_handler(msg); break; default: printf("Error LiDAR Type"); break; } *pcl_out = pl_surf; } void Preprocess::estimateNormals(const pcl::PointCloud::Ptr& cloud) { TicToc t_1; projectPointCloud(cloud); proj_time = t_1.toc(); TicToc t_2; computeRingNormals(cloud); compu_time = t_2.toc(); TicToc t_3; if (!compute_table) { extractCloudAndNormals(cloud); } smooth_time = t_3.toc(); } // //void Preprocess::avia_handler(const livox_ros_driver::CustomMsg::ConstPtr &msg) //{ // pl_surf.clear(); // pl_corn.clear(); // pl_full.clear(); // double t1 = omp_get_wtime(); // int plsize = msg->point_num; // // cout<<"plsie: "<points[i].line < N_SCANS) && ((msg->points[i].tag & 0x30) == 0x10 || (msg->points[i].tag & 0x30) == 0x00)) // { // pl_full[i].x = msg->points[i].x; // pl_full[i].y = msg->points[i].y; // pl_full[i].z = msg->points[i].z; // pl_full[i].intensity = msg->points[i].reflectivity; // pl_full[i].curvature = msg->points[i].offset_time / float(1000000); //use curvature as time of each laser points // // bool is_new = false; // if((abs(pl_full[i].x - pl_full[i-1].x) > 1e-7) // || (abs(pl_full[i].y - pl_full[i-1].y) > 1e-7) // || (abs(pl_full[i].z - pl_full[i-1].z) > 1e-7)) // { // pl_buff[msg->points[i].line].push_back(pl_full[i]); // } // } // } // static int count = 0; // static double time = 0.0; // count ++; // double t0 = omp_get_wtime(); // for(int j=0; j &pl = pl_buff[j]; // plsize = pl.size(); // vector &types = typess[j]; // types.clear(); // types.resize(plsize); // plsize--; // for(uint i=0; ipoints[i].line < N_SCANS) && ((msg->points[i].tag & 0x30) == 0x10 || (msg->points[i].tag & 0x30) == 0x00)) // { // valid_num ++; // if (valid_num % point_filter_num == 0) // { // pl_full[i].x = msg->points[i].x; // pl_full[i].y = msg->points[i].y; // pl_full[i].z = msg->points[i].z; // pl_full[i].intensity = msg->points[i].reflectivity; // pl_full[i].curvature = msg->points[i].offset_time / float(1000000); // use curvature as time of each laser points, curvature unit: ms // // if(((abs(pl_full[i].x - pl_full[i-1].x) > 1e-7) // || (abs(pl_full[i].y - pl_full[i-1].y) > 1e-7) // || (abs(pl_full[i].z - pl_full[i-1].z) > 1e-7)) // && (pl_full[i].x * pl_full[i].x + pl_full[i].y * pl_full[i].y + pl_full[i].z * pl_full[i].z > (blind * blind))) // { // pl_surf.push_back(pl_full[i]); // } // } // } // } // } //} void Preprocess::ouster2velodyne(const pcl::PointCloud& cloud_in, const pcl::PointCloud::Ptr& cloud_out) { int cloud_size = cloud_in.size(); cloud_out->resize(cloud_size); for (int i = 0; i < cloud_size; ++i) { const ouster_ros::Point& p_in = cloud_in[i]; PointXYZIRT& p_out = cloud_out->points[i]; p_out.getVector4fMap() = p_in.getVector4fMap(); p_out.ring = p_in.ring; } } void Preprocess::ouster_handler(const sensor_msgs::PointCloud2::ConstPtr &msg) { pl_surf.clear(); pl_corn.clear(); pl_full.clear(); pcl::PointCloud pl_orig; pcl::fromROSMsg(*msg, pl_orig); pcl::PointCloud::Ptr cloud_tmp(new pcl::PointCloud()); ouster2velodyne(pl_orig, cloud_tmp); if (compute_normal) { TicToc t_nor; estimateNormals(cloud_tmp); // get cloud_with_normal if (runtime_log) { num_scans++; aver_normal_time = aver_normal_time * (num_scans - 1) / num_scans + t_nor.toc() / num_scans; aver_proj_time = aver_proj_time * (num_scans - 1) / num_scans + proj_time / num_scans; aver_compu_time = aver_compu_time * (num_scans - 1) / num_scans + compu_time / num_scans; aver_smooth_time = aver_smooth_time * (num_scans - 1) / num_scans + smooth_time / num_scans; ROS_INFO("[normal] mean project %0.3fms, compute %0.3fms, smooth %0.3fms, total %0.3fms", aver_proj_time, aver_compu_time, aver_smooth_time, aver_normal_time); } } int plsize = pl_orig.size(); pl_corn.reserve(plsize); pl_surf.reserve(plsize); if (feature_enabled) { for (int i = 0; i < N_SCANS; i++) { pl_buff[i].clear(); pl_buff[i].reserve(plsize); } for (uint i = 0; i < plsize; i++) { double range = pl_orig.points[i].x * pl_orig.points[i].x + pl_orig.points[i].y * pl_orig.points[i].y + pl_orig.points[i].z * pl_orig.points[i].z; if (range < (blind * blind)) continue; Eigen::Vector3d pt_vec; PointType added_pt; added_pt.x = pl_orig.points[i].x; added_pt.y = pl_orig.points[i].y; added_pt.z = pl_orig.points[i].z; added_pt.intensity = pl_orig.points[i].intensity; added_pt.normal_x = cloud_with_normal->points[i].normal_x;// normal_x record ring added_pt.normal_y = cloud_with_normal->points[i].normal_y; added_pt.normal_z = cloud_with_normal->points[i].normal_z; double yaw_angle = atan2(added_pt.y, added_pt.x) * 57.3; if (yaw_angle >= 180.0) yaw_angle -= 360.0; if (yaw_angle <= -180.0) yaw_angle += 360.0; added_pt.curvature = pl_orig.points[i].t * time_unit_scale; if(pl_orig.points[i].ring < N_SCANS) { pl_buff[pl_orig.points[i].ring].push_back(added_pt); } } for (int j = 0; j < N_SCANS; j++) { PointCloudXYZI &pl = pl_buff[j]; int linesize = pl.size(); vector &types = typess[j]; types.clear(); types.resize(linesize); linesize--; for (uint i = 0; i < linesize; i++) { types[i].range = sqrt(pl[i].x * pl[i].x + pl[i].y * pl[i].y); vx = pl[i].x - pl[i + 1].x; vy = pl[i].y - pl[i + 1].y; vz = pl[i].z - pl[i + 1].z; types[i].dista = vx * vx + vy * vy + vz * vz; } types[linesize].range = sqrt(pl[linesize].x * pl[linesize].x + pl[linesize].y * pl[linesize].y); give_feature(pl, types); } } else { double time_stamp = msg->header.stamp.toSec(); // cout << "===================================" << endl; // printf("Pt size = %d, N_SCANS = %d\r\n", plsize, N_SCANS); for (int i = 0; i < pl_orig.points.size(); i++) { if (i % point_filter_num != 0) continue; double range = pl_orig.points[i].x * pl_orig.points[i].x + pl_orig.points[i].y * pl_orig.points[i].y + pl_orig.points[i].z * pl_orig.points[i].z; if (range < (blind * blind)) continue; Eigen::Vector3d pt_vec; PointType added_pt; added_pt.x = pl_orig.points[i].x; added_pt.y = pl_orig.points[i].y; added_pt.z = pl_orig.points[i].z; added_pt.intensity = pl_orig.points[i].intensity; added_pt.curvature = pl_orig.points[i].t * time_unit_scale; // curvature unit: ms const int & index = cloud2image[i]; if (compute_normal && index > 0) { int rowIdn, columnIdn; rowIdn = index / image_cols; columnIdn = index - rowIdn * image_cols; const cv::Vec3f &n_cv = normals.at(rowIdn, columnIdn); if (!std::isfinite(n_cv(0)) || !std::isfinite(n_cv(1)) || !std::isfinite(n_cv(2))) continue; added_pt.normal_x = n_cv(0); added_pt.normal_y = n_cv(1); added_pt.normal_z = n_cv(2); } pl_surf.points.push_back(added_pt); } } // pub_func(pl_surf, pub_full, msg->header.stamp); // pub_func(pl_surf, pub_corn, msg->header.stamp); } void Preprocess::velodyne_handler(const sensor_msgs::PointCloud2::ConstPtr &msg) { pl_surf.clear(); pl_corn.clear(); pl_full.clear(); pcl::PointCloud::Ptr pl_orig(new pcl::PointCloud()); // pcl::PointCloud::Ptr pl_orig; pcl::fromROSMsg(*msg, *pl_orig); int plsize = pl_orig->points.size(); // ROS_INFO("cloud input size: %d", plsize); if (compute_normal) { TicToc t_nor; estimateNormals(pl_orig); // get cloud_with_normal if (runtime_log) { num_scans++; aver_normal_time = aver_normal_time * (num_scans - 1) / num_scans + t_nor.toc() / num_scans; aver_proj_time = aver_proj_time * (num_scans - 1) / num_scans + proj_time / num_scans; aver_compu_time = aver_compu_time * (num_scans - 1) / num_scans + compu_time / num_scans; aver_smooth_time = aver_smooth_time * (num_scans - 1) / num_scans + smooth_time / num_scans; ROS_INFO("[normal] mean project %0.3fms, compute %0.3fms, smooth %0.3fms, total %0.3fms", aver_proj_time, aver_compu_time, aver_smooth_time, aver_normal_time); } } if (plsize == 0) return; pl_surf.reserve(plsize); /*** These variables only works when no point timestamps given ***/ double omega_l = 0.361 * SCAN_RATE; // scan angular velocity std::vector is_first(N_SCANS,true); std::vector yaw_fp(N_SCANS, 0.0); // yaw of first scan point std::vector yaw_last(N_SCANS, 0.0); // yaw of last scan point std::vector time_last(N_SCANS, 0.0); // last offset time /*****************************************************************/ if (pl_orig->points[plsize - 1].time > 0) { given_offset_time = true; } else { given_offset_time = false; double yaw_first = atan2(pl_orig->points[0].y, pl_orig->points[0].x) * 57.29578; double yaw_end = yaw_first; int layer_first = pl_orig->points[0].ring; for (uint i = plsize - 1; i > 0; i--) { if (pl_orig->points[i].ring == layer_first) { yaw_end = atan2(pl_orig->points[i].y, pl_orig->points[i].x) * 57.29578; break; } } } if(feature_enabled) { for (int i = 0; i < N_SCANS; i++) { pl_buff[i].clear(); pl_buff[i].reserve(plsize); } for (int i = 0; i < plsize; i++) { PointType added_pt; added_pt.normal_x = cloud_with_normal->points[i].normal_x; added_pt.normal_y = cloud_with_normal->points[i].normal_y; added_pt.normal_z = cloud_with_normal->points[i].normal_z; int layer = pl_orig->points[i].ring; if (layer >= N_SCANS) continue; added_pt.x = pl_orig->points[i].x; added_pt.y = pl_orig->points[i].y; added_pt.z = pl_orig->points[i].z; added_pt.intensity = pl_orig->points[i].intensity; added_pt.curvature = pl_orig->points[i].time * time_unit_scale; // units: ms if (!given_offset_time) { double yaw_angle = atan2(added_pt.y, added_pt.x) * 57.2957; if (is_first[layer]) { // printf("layer: %d; is first: %d", layer, is_first[layer]); yaw_fp[layer]=yaw_angle; is_first[layer]=false; added_pt.curvature = 0.0; yaw_last[layer]=yaw_angle; time_last[layer]=added_pt.curvature; continue; } if (yaw_angle <= yaw_fp[layer]) { added_pt.curvature = (yaw_fp[layer]-yaw_angle) / omega_l; } else { added_pt.curvature = (yaw_fp[layer]-yaw_angle+360.0) / omega_l; } if (added_pt.curvature < time_last[layer]) added_pt.curvature+=360.0/omega_l; yaw_last[layer] = yaw_angle; time_last[layer]=added_pt.curvature; } pl_buff[layer].points.push_back(added_pt); } for (int j = 0; j < N_SCANS; j++) { PointCloudXYZI &pl = pl_buff[j]; int linesize = pl.size(); if (linesize < 2) continue; vector &types = typess[j]; types.clear(); types.resize(linesize); linesize--; for (uint i = 0; i < linesize; i++) { types[i].range = sqrt(pl[i].x * pl[i].x + pl[i].y * pl[i].y); vx = pl[i].x - pl[i + 1].x; vy = pl[i].y - pl[i + 1].y; vz = pl[i].z - pl[i + 1].z; types[i].dista = vx * vx + vy * vy + vz * vz; } types[linesize].range = sqrt(pl[linesize].x * pl[linesize].x + pl[linesize].y * pl[linesize].y); give_feature(pl, types); } } else { for (int i = 0; i < plsize; i++) { PointType added_pt; const PointXYZIRT& orig_pt = pl_orig->points[i]; // cout<<"!!!!!!"< (blind * blind)) { // if (!pointInImage(orig_pt, rowIdn, columnIdn)) // continue; const int & index = cloud2image[i]; if (compute_normal && index > 0) { int rowIdn, columnIdn; rowIdn = index / image_cols; columnIdn = index - rowIdn * image_cols; const cv::Vec3f &n_cv = normals.at(rowIdn, columnIdn); if (!std::isfinite(n_cv(0)) || !std::isfinite(n_cv(1)) || !std::isfinite(n_cv(2))) continue; added_pt.normal_x = n_cv(0); added_pt.normal_y = n_cv(1); added_pt.normal_z = n_cv(2); } pl_surf.points.push_back(added_pt); } } } } pl_surf.resize(pl_surf.points.size()); } bool Preprocess::pointInImage(const PointXYZIRT& point, int& rowIdn, int& columnIdn) { rowIdn = (int)point.ring; // ring，即行索引，此前以用normal_x记录 if (rowIdn < 0 || rowIdn >= N_SCANS) return false; float horizonAngle = atan2(point.y, -point.x); if (horizonAngle < 0) horizonAngle += 2 * M_PI; horizonAngle = horizonAngle * 180 / M_PI; columnIdn = round(horizonAngle/ ang_res_x); //计算列索引， x轴正向为中间列 if (columnIdn < 0 || columnIdn >= image_cols) return false; return true; } void Preprocess::give_feature(pcl::PointCloud &pl, vector &types) { int plsize = pl.size(); int plsize2; if(plsize == 0) { printf("something wrong\n"); return; } uint head = 0; while(types[head].range < blind) { head++; } // Surf plsize2 = (plsize > group_size) ? (plsize - group_size) : 0; Eigen::Vector3d curr_direct(Eigen::Vector3d::Zero()); Eigen::Vector3d last_direct(Eigen::Vector3d::Zero()); uint i_nex = 0, i2; uint last_i = 0; uint last_i_nex = 0; int last_state = 0; int plane_type; for(uint i=head; i0.5) if(last_state==1 && last_direct.norm()>0.1) { double mod = last_direct.transpose() * curr_direct; if(mod>-0.707 && mod<0.707) { types[i].ftype = Edge_Plane; } else { types[i].ftype = Real_Plane; } } i = i_nex - 1; last_state = 1; } else // if(plane_type == 2) { i = i_nex; last_state = 0; } last_i = i2; last_i_nex = i_nex; last_direct = curr_direct; } plsize2 = plsize > 3 ? plsize - 3 : 0; for(uint i=head+3; i=Real_Plane) { continue; } if(types[i-1].dista<1e-16 || types[i].dista<1e-16) { continue; } Eigen::Vector3d vec_a(pl[i].x, pl[i].y, pl[i].z); Eigen::Vector3d vecs[2]; for(int j=0; j<2; j++) { int m = -1; if(j == 1) { m = 1; } if(types[i+m].range < blind) { if(types[i].range > inf_bound) { types[i].edj[j] = Nr_inf; } else { types[i].edj[j] = Nr_blind; } continue; } vecs[j] = Eigen::Vector3d(pl[i+m].x, pl[i+m].y, pl[i+m].z); vecs[j] = vecs[j] - vec_a; types[i].angle[j] = vec_a.dot(vecs[j]) / vec_a.norm() / vecs[j].norm(); if(types[i].angle[j] < jump_up_limit) { types[i].edj[j] = Nr_180; } else if(types[i].angle[j] > jump_down_limit) { types[i].edj[j] = Nr_zero; } } types[i].intersect = vecs[Prev].dot(vecs[Next]) / vecs[Prev].norm() / vecs[Next].norm(); if(types[i].edj[Prev]==Nr_nor && types[i].edj[Next]==Nr_zero && types[i].dista>0.0225 && types[i].dista>4*types[i-1].dista) { if(types[i].intersect > cos160) { if(edge_jump_judge(pl, types, i, Prev)) { types[i].ftype = Edge_Jump; } } } else if(types[i].edj[Prev]==Nr_zero && types[i].edj[Next]== Nr_nor && types[i-1].dista>0.0225 && types[i-1].dista>4*types[i].dista) { if(types[i].intersect > cos160) { if(edge_jump_judge(pl, types, i, Next)) { types[i].ftype = Edge_Jump; } } } else if(types[i].edj[Prev]==Nr_nor && types[i].edj[Next]==Nr_inf) { if(edge_jump_judge(pl, types, i, Prev)) { types[i].ftype = Edge_Jump; } } else if(types[i].edj[Prev]==Nr_inf && types[i].edj[Next]==Nr_nor) { if(edge_jump_judge(pl, types, i, Next)) { types[i].ftype = Edge_Jump; } } else if(types[i].edj[Prev]>Nr_nor && types[i].edj[Next]>Nr_nor) { if(types[i].ftype == Nor) { types[i].ftype = Wire; } } } plsize2 = plsize-1; double ratio; for(uint i=head+1; i types[i].dista) { ratio = types[i-1].dista / types[i].dista; } else { ratio = types[i].dista / types[i-1].dista; } if(types[i].intersect &types, uint i_cur, uint &i_nex, Eigen::Vector3d &curr_direct) { double group_dis = disA*types[i_cur].range + disB; group_dis = group_dis * group_dis; // i_nex = i_cur; double two_dis; vector disarr; disarr.reserve(20); for(i_nex=i_cur; i_nex= pl.size()) || (i_nex >= pl.size())) break; if(types[i_nex].range < blind) { curr_direct.setZero(); return 2; } vx = pl[i_nex].x - pl[i_cur].x; vy = pl[i_nex].y - pl[i_cur].y; vz = pl[i_nex].z - pl[i_cur].z; two_dis = vx*vx + vy*vy + vz*vz; if(two_dis >= group_dis) { break; } disarr.push_back(types[i_nex].dista); i_nex++; } double leng_wid = 0; double v1[3], v2[3]; for(uint j=i_cur+1; j= pl.size()) || (i_cur >= pl.size())) break; v1[0] = pl[j].x - pl[i_cur].x; v1[1] = pl[j].y - pl[i_cur].y; v1[2] = pl[j].z - pl[i_cur].z; v2[0] = v1[1]*vz - vy*v1[2]; v2[1] = v1[2]*vx - v1[0]*vz; v2[2] = v1[0]*vy - vx*v1[1]; double lw = v2[0]*v2[0] + v2[1]*v2[1] + v2[2]*v2[2]; if(lw > leng_wid) { leng_wid = lw; } } if((two_dis*two_dis/leng_wid) < p2l_ratio) { curr_direct.setZero(); return 0; } uint disarrsize = disarr.size(); for(uint j=0; j=limit_maxmid || dismid_min>=limit_midmin) { curr_direct.setZero(); return 0; } } else { double dismax_min = disarr[0] / disarr[disarrsize-2]; if(dismax_min >= limit_maxmin) { curr_direct.setZero(); return 0; } } curr_direct << vx, vy, vz; curr_direct.normalize(); return 1; } bool Preprocess::edge_jump_judge(const PointCloudXYZI &pl, vector &types, uint i, Surround nor_dir) { if(nor_dir == 0) { if(types[i-1].rangeedgea*d2 || (d1-d2)>edgeb) { return false; } return true; } void Preprocess::initNormalEstimator() { // image_cols = Horizon_SCAN / point_filter_num; image_cols = Horizon_SCAN; range_image = RangeImage(N_SCANS, image_cols); ang_res_x = 360.0/float(image_cols); if (!compute_table) { if (!range_image.loadLookupTable(ring_table_dir, "ring" + std::to_string(N_SCANS))) { ROS_ERROR("Wrong path to ring normal M file, EXIT."); } } rangeMat = cv::Mat(N_SCANS, image_cols, CV_32F, cv::Scalar::all(FLT_MAX)); } ================================================ FILE: src/preprocess.h ================================================ #include #include #include //#include #include "ring_fals//range_image.h" #include using namespace std; #define IS_VALID(a) ((abs(a)>1e8) ? true : false) typedef pcl::PointXYZINormal PointType; //typedef ikdTree_PointType PointType; typedef pcl::PointCloud PointCloudXYZI; enum LID_TYPE{AVIA = 1, VELODYNE, OUSTER}; //{1, 2, 3} enum TIME_UNIT{SEC = 0, MS = 1, US = 2, NS = 3}; enum Feature{Nor, Poss_Plane, Real_Plane, Edge_Jump, Edge_Plane, Wire, ZeroPoint}; enum Surround{Prev, Next}; enum E_jump{Nr_nor, Nr_zero, Nr_180, Nr_inf, Nr_blind}; struct orgtype { double range; double dista; double angle[2]; double intersect; E_jump edj[2]; Feature ftype; orgtype() { range = 0; edj[Prev] = Nr_nor; edj[Next] = Nr_nor; ftype = Nor; intersect = 2; } }; namespace velodyne_ros { struct EIGEN_ALIGN16 Point { PCL_ADD_POINT4D; float intensity; float time; uint16_t ring; EIGEN_MAKE_ALIGNED_OPERATOR_NEW }; } // namespace velodyne_ros POINT_CLOUD_REGISTER_POINT_STRUCT(velodyne_ros::Point, (float, x, x) (float, y, y) (float, z, z) (float, intensity, intensity) (float, time, time) (uint16_t, ring, ring) ) namespace ouster_ros { struct EIGEN_ALIGN16 Point { PCL_ADD_POINT4D; float intensity; uint32_t t; uint16_t reflectivity; uint8_t ring; uint16_t ambient; uint32_t range; EIGEN_MAKE_ALIGNED_OPERATOR_NEW }; } // namespace ouster_ros // clang-format off POINT_CLOUD_REGISTER_POINT_STRUCT(ouster_ros::Point, (float, x, x) (float, y, y) (float, z, z) (float, intensity, intensity) // use std::uint32_t to avoid conflicting with pcl::uint32_t (std::uint32_t, t, t) (std::uint16_t, reflectivity, reflectivity) (std::uint8_t, ring, ring) (std::uint16_t, ambient, ambient) (std::uint32_t, range, range) ) class Preprocess { public: // EIGEN_MAKE_ALIGNED_OPERATOR_NEW Preprocess(); ~Preprocess(); // void process(const livox_ros_driver::CustomMsg::ConstPtr &msg, PointCloudXYZI::Ptr &pcl_out); void process(const sensor_msgs::PointCloud2::ConstPtr &msg, PointCloudXYZI::Ptr &pcl_out); void set(bool feat_en, int lid_type, double bld, int pfilt_num); void initNormalEstimator(); // sensor_msgs::PointCloud2::ConstPtr pointcloud; PointCloudXYZI pl_full, pl_corn, pl_surf; PointCloudXYZI::Ptr cloud_with_normal; PointCloudXYZI pl_buff[128]; //maximum 128 line lidar vector typess[128]; //maximum 128 line lidar float time_unit_scale; int lidar_type, point_filter_num, N_SCANS, SCAN_RATE, time_unit; double blind; bool feature_enabled, given_offset_time; ros::Publisher pub_full, pub_surf, pub_corn; // parameters for normal estimation RangeImage range_image; bool compute_table = false; int Horizon_SCAN, image_cols; string ring_table_dir; bool runtime_log = false; bool compute_normal = false; private: // void avia_handler(const livox_ros_driver::CustomMsg::ConstPtr &msg); void ouster_handler(const sensor_msgs::PointCloud2::ConstPtr &msg); void velodyne_handler(const sensor_msgs::PointCloud2::ConstPtr &msg); void give_feature(PointCloudXYZI &pl, vector &types); void pub_func(PointCloudXYZI &pl, const ros::Time &ct); int plane_judge(const PointCloudXYZI &pl, vector &types, uint i, uint &i_nex, Eigen::Vector3d &curr_direct); bool small_plane(const PointCloudXYZI &pl, vector &types, uint i_cur, uint &i_nex, Eigen::Vector3d &curr_direct); bool edge_jump_judge(const PointCloudXYZI &pl, vector &types, uint i, Surround nor_dir); void projectPointCloud(const pcl::PointCloud::Ptr& cloud); void computeRingNormals(const pcl::PointCloud::Ptr& cloud); void extractCloudAndNormals(const pcl::PointCloud::Ptr& cloud); void flipNormalsTowardCenterAndNormalized(const cv::Vec3f& center, const pcl::PointCloud::Ptr& cloud, cv::Mat& image_normals); void NormalizeNormals(cv::Mat& image_normals); void saveNormalPCD(const std::string& file_name, const pcl::PointCloud::Ptr& cloud, const cv::Mat& normals_mat); void estimateNormals(const pcl::PointCloud::Ptr& cloud); void estimateNormals(const pcl::PointCloud::Ptr& cloud); bool pointInImage(const PointXYZIRT& point, int& row, int& col); void ouster2velodyne(const pcl::PointCloud& cloud_in, const pcl::PointCloud::Ptr& cloud_out); int group_size; double disA, disB, inf_bound; double limit_maxmid, limit_midmin, limit_maxmin; double p2l_ratio; double jump_up_limit, jump_down_limit; double cos160; double edgea, edgeb; double smallp_intersect, smallp_ratio; double vx, vy, vz; // parameters for normal estimation cv::Mat rangeMat; cv::Mat normals; //record ring normals cv::Mat plane_residual; //record ring normals residual in normal estimation std::vector image2cloud; std::vector cloud2image; double aver_normal_time = 0.0, aver_proj_time = 0.0, aver_compu_time = 0.0, aver_smooth_time = 0.0; double proj_time = 0.0, compu_time = 0.0, smooth_time = 0.0; int num_scans = 0; float ang_res_x; }; ================================================ FILE: src/ring_fals/Image_normals.hpp ================================================ /* * Software License Agreement (BSD License) * * Copyright (c) 2012, Willow Garage, Inc. * All rights reserved. * * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions * are met: * * * Redistributions of source code must retain the above copyright * notice, this list of conditions and the following disclaimer. * * Redistributions in binary form must reproduce the above * copyright notice, this list of conditions and the following * disclaimer in the documentation and/or other materials provided * with the distribution. * * Neither the name of Willow Garage, Inc. nor the names of its * contributors may be used to endorse or promote products derived * from this software without specific prior written permission. * * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE * POSSIBILITY OF SUCH DAMAGE. * */ #include "precomp.h" #include /** Just compute the norm of a vector * @param vec a vector of size 3 and any type T * @return */ template T inline norm_vec(const cv::Vec &vec) { return std::sqrt(vec[0] * vec[0] + vec[1] * vec[1] + vec[2] * vec[2]); } /** Modify normals to make sure they point towards the camera * @param normals */ template inline void signNormal(const cv::Vec & normal_in, cv::Vec & normal_out) { cv::Vec res; if (normal_in[2] > 0) res = -normal_in / norm_vec(normal_in); else res = normal_in / norm_vec(normal_in); normal_out[0] = res[0]; normal_out[1] = res[1]; normal_out[2] = res[2]; } /** Normalized normals * @param normals */ template inline void normalizedNormal(const cv::Vec & normal_in, cv::Vec & normal_out) { normal_out = normal_in / norm_vec(normal_in); } /** Given 3d points, compute their distance to the origin * @param points * @return */ template cv::Mat_ computeRadius(const cv::Mat &points) { typedef cv::Vec PointT; // Compute the cv::Size size(points.cols, points.rows); cv::Mat_ r(size); if (points.isContinuous()) size = cv::Size(points.cols * points.rows, 1); for (int y = 0; y < size.height; ++y) { const PointT* point = points.ptr < PointT > (y), *point_end = points.ptr < PointT > (y) + size.width; T * row = r[y]; for (; point != point_end; ++point, ++row) *row = norm_vec(*point); } return r; } // Compute theta and phi according to equation 3 of // ``Fast and Accurate Computation of Surface Normals from Range Images`` // by H. Badino, D. Huber, Y. Park and T. Kanade template void computeThetaPhi(int rows, int cols, const cv::Matx& K, cv::Mat &cos_theta, cv::Mat &sin_theta, cv::Mat &cos_phi, cv::Mat &sin_phi) { // Create some bogus coordinates cv::Mat depth_image = K(0, 0) * cv::Mat_ < T > ::ones(rows, cols); //fx cv::Mat points3d; cv::rgbd::depthTo3d(depth_image, cv::Mat(K), points3d); typedef cv::Vec Vec3T; cos_theta = cv::Mat_ < T > (rows, cols); sin_theta = cv::Mat_ < T > (rows, cols); cos_phi = cv::Mat_ < T > (rows, cols); sin_phi = cv::Mat_ < T > (rows, cols); cv::Mat r = computeRadius(points3d); for (int y = 0; y < rows; ++y) { T *row_cos_theta = cos_theta.ptr < T > (y), *row_sin_theta = sin_theta.ptr < T > (y); T *row_cos_phi = cos_phi.ptr < T > (y), *row_sin_phi = sin_phi.ptr < T > (y); const Vec3T * row_points = points3d.ptr < Vec3T > (y), *row_points_end = points3d.ptr < Vec3T > (y) + points3d.cols; const T * row_r = r.ptr < T > (y); for (; row_points < row_points_end; ++row_cos_theta, ++row_sin_theta, ++row_cos_phi, ++row_sin_phi, ++row_points, ++row_r) { // In the paper, z goes away from the camera, y goes down, x goes right // OpenCV has the same conventions // Theta goes from z to x (and actually goes from -pi/2 to pi/2, phi goes from z to y float theta = (float)std::atan2(row_points->val[0], row_points->val[2]); //theta，x/z *row_cos_theta = std::cos(theta); *row_sin_theta = std::sin(theta); float phi = (float)std::asin(row_points->val[1] / (*row_r)); //phi， y/r *row_cos_phi = std::cos(phi); *row_sin_phi = std::sin(phi); } } } //template class LIDAR_FALS { public: typedef cv::Matx Mat33T; typedef cv::Vec Vec9T; typedef cv::Vec Vec3T; //(depth是数据类型 LIDAR_FALS(int rows, int cols, int window_size, int depth, const cv::Mat &K) {} LIDAR_FALS(int rows, int cols, int window_size) : rows_(rows) , cols_(cols) , window_size_(window_size) {} LIDAR_FALS() {} ~LIDAR_FALS() {} ///计算M逆 /** Compute cached data */ virtual void cache() { std::cout << "build look up table.\n"; // Compute theta and phi according to equation 3 cv::Mat cos_theta, sin_theta, cos_phi, sin_phi; computeThetaPhi(rows_, cols_, K_, cos_theta, sin_theta, cos_phi, sin_phi); computeMInverse(cos_theta, sin_theta, cos_phi, sin_phi); } void computeMInverse(cv::Mat &cos_theta, cv::Mat &sin_theta, cv::Mat &cos_phi, cv::Mat &sin_phi) { // Compute all the v_i for every points std::vector channels(3); channels[0] = cos_phi.mul(cos_theta); channels[1] = -cos_phi.mul(sin_theta); channels[2] = sin_phi; merge(channels, V_); // Compute M cv::Mat_ M(rows_, cols_); Mat33T VVt; const Vec3T * vec = V_[0]; Vec9T * M_ptr = M[0], *M_ptr_end = M_ptr + rows_ * cols_; for (; M_ptr != M_ptr_end; ++vec, ++M_ptr) { VVt = (*vec) * vec->t(); //v * v_t *M_ptr = Vec9T(VVt.val); } ///todo BorderTypes::BORDER_TRANSPARENT, error int border_type = cv::BorderTypes::BORDER_TRANSPARENT; boxFilter(M, M, M.depth(), cv::Size(window_size_, window_size_), cv::Point(-1, -1), false); // Compute M's inverse Mat33T M_inv; M_inv_.create(rows_, cols_); Vec9T * M_inv_ptr = M_inv_[0]; for (M_ptr = &M(0); M_ptr != M_ptr_end; ++M_inv_ptr, ++M_ptr) { // We have a semi-definite matrix invert(Mat33T(M_ptr->val), M_inv, cv::DECOMP_CHOLESKY); *M_inv_ptr = Vec9T(M_inv.val); } } void saveMInverse(std::string dir, std::string filename) { std::vector mats(9); for (int i = 0; i < 9; ++i) { mats[i].create(M_inv_.rows, M_inv_.cols, CV_32F); } cv::Mat mat_vv(M_inv_.rows, M_inv_.cols, CV_32FC3); for (int i = 0; i < M_inv_.rows; ++i) { for (int j = 0; j < M_inv_.cols; ++j) { const Vec9T& tmp(M_inv_.at(i, j)); for (int k = 0; k < 9; ++k) { mats[k].at(i, j) = tmp(k); } mat_vv.at(i, j) = V_.at(i, j); } } //save M inverse for (int i = 0; i < 9; ++i) { std::string file_id(std::to_string(i)); cv::FileStorage fs(dir + "/" + filename + "_M_" + file_id + ".xml", cv::FileStorage::WRITE); fs << filename + "_" + file_id << mats[i]; fs.release(); } //save v v_t cv::FileStorage fs(dir + "/" + filename + "_v" + + ".xml", cv::FileStorage::WRITE); fs << filename + "_vv" << mat_vv; fs.release(); } bool loadMInverse(std::string dir, std::string filename) { std::vector mats(9); for (int i = 0; i < 9; ++i) { mats[i].create(rows_, cols_, CV_32F); } for (int i = 0; i < 9; ++i) { std::string file_id(std::to_string(i)); cv::FileStorage fs(dir + "/" + filename + "_M_" + file_id + ".xml", cv::FileStorage::READ); if(!fs.isOpened()) { // std::cerr << "ERROR: Wrong path to ring normal M file" << std::endl; return false; } fs[filename + "_" + file_id] >> mats[i]; fs.release(); } M_inv_.create(rows_, cols_); for (int i = 0; i < rows_; ++i) { for (int j = 0; j < cols_; ++j) { Vec9T& tmp(M_inv_.at(i, j)); for (int k = 0; k < 9; ++k) { tmp(k) = mats[k].at(i, j); } } } V_.create(rows_, cols_); cv::FileStorage fs(dir + "/" + filename + "_v" + + ".xml", cv::FileStorage::READ); fs[filename + "_vv"] >> V_; fs.release(); return true; } /** Compute the normals * @param r * @return */ virtual void compute(const cv::Mat &r, cv::Mat & normals) const { // Compute B cv::Mat_ B(rows_, cols_); const float* row_r = r.ptr < float > (0), *row_r_end = row_r + rows_ * cols_; const Vec3T *row_V = V_[0]; Vec3T *row_B = B[0]; for (; row_r != row_r_end; ++row_r, ++row_B, ++row_V) { if (*row_r==FLT_MAX) *row_B = Vec3T(); else *row_B = (*row_V) / (*row_r); //v_i / r_i } ///todo BorderTypes::BORDER_TRANSPARENT, error // Apply a box filter to B boxFilter(B, B, B.depth(), cv::Size(window_size_, window_size_), cv::Point(-1, -1), false); // compute the Minv*B products row_r = r.ptr < float > (0); const Vec3T * B_vec = B[0]; const Mat33T * M_inv = reinterpret_cast(M_inv_.ptr(0)); Vec3T *normal = normals.ptr(0); for (; row_r != row_r_end; ++row_r, ++B_vec, ++normal, ++M_inv) if (*row_r==FLT_MAX) { (*normal)[0] = *row_r; (*normal)[1] = *row_r; (*normal)[2] = *row_r; } else { Mat33T Mr = *M_inv; Vec3T Br = *B_vec; Vec3T MBr(Mr(0, 0) * Br[0] + Mr(0, 1)*Br[1] + Mr(0, 2)*Br[2], Mr(1, 0) * Br[0] + Mr(1, 1)*Br[1] + Mr(1, 2)*Br[2], Mr(2, 0) * Br[0] + Mr(2, 1)*Br[1] + Mr(2, 2)*Br[2]); //actually, can not flip normal here, because the point position is unknown signNormal(MBr, *normal); } } /** Compute the normals * @param r * @return */ virtual void compute(const cv::Mat &r, cv::Mat & normals, cv::Mat & residual) const { // Compute B cv::Mat_ B(rows_, cols_); const float* row_r = r.ptr < float > (0), *row_r_end = row_r + rows_ * cols_; const Vec3T *row_V = V_[0]; Vec3T *row_B = B[0]; for (; row_r != row_r_end; ++row_r, ++row_B, ++row_V) { if (*row_r==FLT_MAX) *row_B = Vec3T(); else *row_B = (*row_V) / (*row_r); //v_i / r_i } ///todo BorderTypes::BORDER_TRANSPARENT, error // Apply a box filter to B boxFilter(B, B, B.depth(), cv::Size(window_size_, window_size_), cv::Point(-1, -1), false); // compute the Minv*B products row_r = r.ptr < float > (0); const Vec3T * B_vec = B[0]; const Mat33T * M_inv = reinterpret_cast(M_inv_.ptr(0)); Vec3T *normal = normals.ptr(0); for (; row_r != row_r_end; ++row_r, ++B_vec, ++normal, ++M_inv) { if (*row_r==FLT_MAX) { (*normal)[0] = *row_r; (*normal)[1] = *row_r; (*normal)[2] = *row_r; } else { Mat33T Mr = *M_inv; Vec3T Br = *B_vec; Vec3T MBr(Mr(0, 0) * Br[0] + Mr(0, 1) * Br[1] + Mr(0, 2) * Br[2], Mr(1, 0) * Br[0] + Mr(1, 1) * Br[1] + Mr(1, 2) * Br[2], Mr(2, 0) * Br[0] + Mr(2, 1) * Br[1] + Mr(2, 2) * Br[2]); // signNormal(MBr, *normal); normalizedNormal(MBr, *normal); } } // { // row_V = V_[0]; // row_r = r.ptr < float > (0); // Vec3T *normal = normals.ptr(0); // float *res = residual.ptr(0); // for (; row_r != row_r_end; ++row_r, ++normal, ++row_V, ++res) { // float e = row_V->dot(*normal) - (1.0 / *row_r); // *res = e * e; // } // } } void setRowsCols(const int& rows, const int& cols) { rows_ = rows; cols_ = cols; } private: int rows_, cols_; int depth_ = CV_32F; cv::Mat K_, K_ori_; int window_size_; cv::Mat_ V_; //sin(theta) * cos(phi), sin(phi), cos(theta) * cos(phi) cv::Mat_ M_inv_; //M^-1 }; ================================================ FILE: src/ring_fals/precomp.h ================================================ /*M/////////////////////////////////////////////////////////////////////////////////////// // // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. // // By downloading, copying, installing or using the software you agree to this license. // If you do not agree to this license, do not download, install, // copy or use the software. // // // License Agreement // For Open Source Computer Vision Library // // Copyright (C) 2000-2008, Intel Corporation, all rights reserved. // Copyright (C) 2009, Willow Garage Inc., all rights reserved. // Third party copyrights are property of their respective owners. // // Redistribution and use in source and binary forms, with or without modification, // are permitted provided that the following conditions are met: // // * Redistribution's of source code must retain the above copyright notice, // this list of conditions and the following disclaimer. // // * Redistribution's in binary form must reproduce the above copyright notice, // this list of conditions and the following disclaimer in the documentation // and/or other materials provided with the distribution. // // * The name of the copyright holders may not be used to endorse or promote products // derived from this software without specific prior written permission. // // This software is provided by the copyright holders and contributors "as is" and // any express or implied warranties, including, but not limited to, the implied // warranties of merchantability and fitness for a particular purpose are disclaimed. // In no event shall the Intel Corporation or contributors be liable for any direct, // indirect, incidental, special, exemplary, or consequential damages // (including, but not limited to, procurement of substitute goods or services; // loss of use, data, or profits; or business interruption) however caused // and on any theory of liability, whether in contract, strict liability, // or tort (including negligence or otherwise) arising in any way out of // the use of this software, even if advised of the possibility of such damage. // //M*/ #ifndef __OPENCV_PRECOMP_H__ #define __OPENCV_PRECOMP_H__ #include "opencv2/rgbd.hpp" #include "opencv2/calib3d.hpp" #include "opencv2/imgproc.hpp" #include "opencv2/core/utility.hpp" //#include "opencv2/core/private.hpp" #include #include #include #include #endif ================================================ FILE: src/ring_fals/range_image.cpp ================================================ // // Created by hk on 12/8/22. // #include "range_image.h" void RangeImage::createFromRings(const pcl::PointCloud::Ptr& cloud) { if (rows_ == 0 || cols_ == 0) { std::cout << "rows_ or cols_ == 0, can't create range image.\n"; return; } range_image = cv::Mat(rows_, cols_, CV_32F, range_init); cloud_id_image.resize(rows_ * cols_, -1); int cloudSize = (int)cloud->points.size(); // range image projection for (int i = 0; i < cloudSize; ++i) { // PointType thisPoint; // thisPoint.x = laserCloudIn->points[i].x; // thisPoint.y = laserCloudIn->points[i].y; // thisPoint.z = laserCloudIn->points[i].z; // thisPoint.intensity = laserCloudIn->points[i].intensity; const PointXYZIRT & thisPoint = cloud->points[i]; int rowIdn = cloud->points[i].ring; // ring if (rowIdn < 0 || rowIdn >= rows_) continue; if (downsampleRate > 0 && rowIdn % downsampleRate != 0) //采样行间隔 continue; // float horizonAngle = atan2(thisPoint.x, thisPoint.y) * 180 / M_PI; // static float ang_res_x = 360.0/float(cols_); // 360 / 1800 = 0.2 float horizonAngle = atan2(thisPoint.y, -thisPoint.x); if (horizonAngle < 0) horizonAngle += 2 * M_PI; horizonAngle = horizonAngle * 180 / M_PI; int columnIdn = round(horizonAngle/ ang_res_x); if (columnIdn < 0 || columnIdn >= cols_) continue; float range = sqrt(thisPoint.x * thisPoint.x + thisPoint.y * thisPoint.y +thisPoint.z * thisPoint.z);// sqrt(x^2 + y^2 + z^2) if (range < range_min || range > range_max) continue; if (range_image.at(rowIdn, columnIdn) != FLT_MAX) continue; // for the amsterdam dataset // if (range < 6.0 && rowIdn <= 7 && (columnIdn >= 1600 || columnIdn <= 200)) // continue; // if (thisPoint.z < -2.0) // continue; range_image.at(rowIdn, columnIdn) = range; // if (input_value_max < range) input_value_max = range; // thisPoint = deskewPoint(&thisPoint, laserCloudIn->points[i].time); // Velodyne // // thisPoint = deskewPoint(&thisPoint, (float)laserCloudIn->points[i].t / 1000000000.0); // Ouster int index = columnIdn + rowIdn * cols_; // cloud_id_image[index] = i; } } void RangeImage::saveRangeImage(const std::string &file) { cv::Mat output = cv::Mat(range_image.rows, range_image.cols, CV_8U , 255); for (int i = 0; i < range_image.rows; ++i) { for (int j = 0; j < range_image.cols; ++j) { if (range_image.at(i, j) < 50.0) output.at(i, j) = range_image.at(i, j) / 50 * 255.0; else output.at(i, j) = 255; } } cv::imwrite(file, output); } void RangeImage::computeNormals(cv::OutputArray normals_out) { // cv::Mat points3d = points3d_in.getMat(); // Get the normals normals_out.create(rows_, cols_, CV_MAKETYPE(depth_, 3)); // if (points3d_in.empty()) // return; cv::Mat normals = normals_out.getMat(); lidar_fals.compute(range_image, normals); }; //in lidar frame void RangeImage::createFromCloud(const int& num_bins_v, const int& num_bins_h, const pcl::PointCloud::Ptr& cloud) { float bin_res_v = 180.0 / (float)num_bins_v; // vertical float bin_res_h = 360.0 / (float)num_bins_h; // horizon range_image = cv::Mat(num_bins_v, num_bins_h, CV_32F, cv::Scalar::all(FLT_MAX)); cloud_id_image.resize(rows_ * cols_, -1); //lidar系下点云 for (int i = 0; i < (int)cloud->size(); ++i) { const PointType &p = cloud->points[i]; // find row id in range image float row_angle = atan2(p.z, sqrt(p.x * p.x + p.y * p.y)); // degrees, bottom -> up, -90 -> 0 -> 90 row_angle = -row_angle * 180.0 / M_PI + 90.0; //bottom -> up, 180 -> 90 -> 0 int row_id = round(row_angle / bin_res_v); // find column id in range image float col_angle = atan2(p.y, -p.x) * 180.0 / M_PI; // degrees, back -> left -> front -> right, 0 -> 360 if (col_angle < 0) col_angle += 360; int col_id = round(col_angle / bin_res_h); // id may be out of boundary if (row_id < 0 || row_id >= num_bins_v || col_id < 0 || col_id >= num_bins_h) continue; // only keep points that's closer float dist = sqrt(p.x * p.x + p.y * p.y + p.z * p.z); if (dist < range_image.at(row_id, col_id)) { range_image.at(row_id, col_id) = dist; int index = col_id + row_id * cols_; // cloud_id_image[index] = i; } } } void RangeImage::computeLookupTable() { if (range_image.empty()) return; } void RangeImage::createAndBuildTableFromRings(const int &num_rings, const int &num_horizon_scan, const pcl::PointCloud::Ptr &cloud) { if (rows_ != num_rings || cols_ != num_horizon_scan) { std::cout << "rows_ != num_rings || cols_ != num_horizon_scan. reset rows_ and cols\n"; rows_ = num_rings; cols_ = num_horizon_scan; lidar_fals.setRowsCols(num_rings, num_horizon_scan); } createAndBuildTableFromRings(cloud); } void RangeImage::createAndBuildTableFromRings(const pcl::PointCloud::Ptr &cloud) { if (rows_ == 0 || cols_ == 0) { std::cout << "rows_ == 0 or cols_ == 0, can't create range image.\n"; return; } int num_valid_bins = 0; range_image = cv::Mat(rows_, cols_, CV_32F, range_init); cloud_id_image.resize(rows_ * cols_, -1); cv::Mat cos_theta = cv::Mat(rows_, cols_, CV_32F); cv::Mat sin_theta = cv::Mat(rows_, cols_, CV_32F); cv::Mat cos_phi = cv::Mat(rows_, cols_, CV_32F); cv::Mat sin_phi = cv::Mat(rows_, cols_, CV_32F); int cloudSize = (int)cloud->points.size(); // range image projection for (int i = 0; i < cloudSize; ++i) { const PointXYZIRT & thisPoint = cloud->points[i]; int rowIdn = cloud->points[i].ring; // ring， if (rowIdn < 0 || rowIdn >= rows_) continue; if (downsampleRate > 0 && rowIdn % downsampleRate != 0) // continue; float horizonAngle = atan2(thisPoint.x, thisPoint.y) * 180 / M_PI; //theta static float ang_res_x = 360.0/float(cols_); // 360 / 1800 = 0.2 int columnIdn = -round((horizonAngle-90.0)/ang_res_x) + cols_/2; // if (columnIdn >= cols_) columnIdn -= cols_; if (columnIdn < 0 || columnIdn >= cols_) continue; float range = sqrt(thisPoint.x * thisPoint.x + thisPoint.y * thisPoint.y +thisPoint.z * thisPoint.z);// sqrt(x^2 + y^2 + z^2) if (range < range_min || range > range_max) continue; if (range_image.at(rowIdn, columnIdn) != FLT_MAX) // continue; // for the amsterdam dataset // if (range < 6.0 && rowIdn <= 7 && (columnIdn >= 1600 || columnIdn <= 200)) // continue; // if (thisPoint.z < -2.0) // continue; range_image.at(rowIdn, columnIdn) = range; // // if (input_value_max < range) // input_value_max = range; //theta, phi used for building lookup table if (!table_valid) { float theta = (float) std::atan2(-thisPoint.y, thisPoint.x); //，-y / x float phi = (float) std::asin(thisPoint.z / range); //， z/r cos_theta.at(rowIdn, columnIdn) = std::cos(theta); sin_theta.at(rowIdn, columnIdn) = std::sin(theta); cos_phi.at(rowIdn, columnIdn) = std::cos(phi); sin_phi.at(rowIdn, columnIdn) = std::sin(phi); } // thisPoint = deskewPoint(&thisPoint, laserCloudIn->points[i].time); // Velodyne // // thisPoint = deskewPoint(&thisPoint, (float)laserCloudIn->points[i].t / 1000000000.0); // Ouster int index = columnIdn + rowIdn * cols_; //点在深度图像中的索引 cloud_id_image[index] = i; // fullCloud->points[index] = thisPoint; } //save mat for debug { // cv::Mat output = cv::Mat(rows_, cols_, CV_8U, 255); // for (int i = 0; i < rows_; ++i) { // for (int j = 0; j < cols_; ++j) { // if (range_image.at(i, j) < 100.0) // output.at(i, j) = range_image.at(i, j) / input_value_max * 255.0; // } // } // cv::imwrite("/tmp/cos_theta.jpg", cos_theta); } //todo compute M matrix and M inverse if (!table_valid) { lidar_fals.computeMInverse(cos_theta, sin_theta, cos_phi, sin_phi); table_valid = true; } } void RangeImage::buildTableFromRings(const pcl::PointCloud::Ptr &cloud) { if (rows_ == 0 || cols_ == 0) { std::cout << "rows_ == 0 or cols_ == 0, can't create range image.\n"; return; } // int num_valid_bins = 0; // range_image = cv::Mat(rows_, cols_, CV_32F, range_init); cloud_id_image.resize(rows_ * cols_, -1); int cloudSize = (int)cloud->points.size(); static float ang_res_x = 360.0 / float(cols_); // 360 / 1800 = 0.2 // range image projection for (int i = 0; i < cloudSize; ++i) { const PointXYZIRT & thisPoint = cloud->points[i]; int rowIdn = cloud->points[i].ring; // ring， if (rowIdn < 0 || rowIdn >= rows_) continue; float horizonAngle = atan2(thisPoint.y, -thisPoint.x); //theta if (horizonAngle < 0) horizonAngle += 2 * M_PI; horizonAngle = horizonAngle * 180 / M_PI; int columnIdn = round(horizonAngle/ ang_res_x); if (columnIdn < 0 || columnIdn >= cols_) continue; float range = sqrt(thisPoint.x * thisPoint.x + thisPoint.y * thisPoint.y +thisPoint.z * thisPoint.z);// sqrt(x^2 + y^2 + z^2) if (range < range_min || range > range_max) continue; //theta, phi used for building lookup table float theta = (float) std::atan2(-thisPoint.y, thisPoint.x); //，-y / x float phi = (float) std::asin(thisPoint.z / range); //， z/r int index = columnIdn + rowIdn * cols_; // float num_p = static_cast(num_per_pixel[index]); float num_p_new = num_p + 1; cos_theta.at(rowIdn, columnIdn) = (num_p * cos_theta.at(rowIdn, columnIdn) + std::cos(theta)) / num_p_new; sin_theta.at(rowIdn, columnIdn) = (num_p * sin_theta.at(rowIdn, columnIdn) + std::sin(theta)) / num_p_new; cos_phi.at(rowIdn, columnIdn) = (num_p * cos_phi.at(rowIdn, columnIdn) + std::cos(phi)) / num_p_new; sin_phi.at(rowIdn, columnIdn) = (num_p * sin_phi.at(rowIdn, columnIdn) + std::sin(phi)) / num_p_new; ++num_per_pixel[index]; // cloud_id_image[index] = i; } ++num_cloud; //todo compute M matrix and M inverse if (num_cloud < min_table_cloud) return; // lidar_fals.computeMInverse(cos_theta, sin_theta, cos_phi, sin_phi); } void RangeImage::buildTableFromRings(const int &num_rings, const int &num_horizon_scan, const pcl::PointCloud::Ptr &cloud) { if (rows_ != num_rings || cols_ != num_horizon_scan) { std::cout << "rows_ != num_rings || cols_ != num_horizon_scan. reset rows_ and cols\n"; rows_ = num_rings; cols_ = num_horizon_scan; lidar_fals.setRowsCols(num_rings, num_horizon_scan); } buildTableFromRings(cloud); } void RangeImage::buildTableFromCloud() { float bin_res_v = M_PI / (float)rows_; // vertical(radian) float bin_res_h = 2.0 * M_PI / (float)cols_; // horizon(radian) //lidar系下点云 for (int i = 0; i < rows_; ++i) { float phi = 0.5 * M_PI - ((float)i + 0.5) * bin_res_v; float c_phi = std::cos(phi); float s_phi = std::sin(phi); for (int j = 0; j < cols_; ++j) { float theta = ((float)j + 0.5) * bin_res_h - M_PI; cos_theta.at(i, j) = std::cos(theta); sin_theta.at(i, j) = std::sin(theta); cos_phi.at(i, j) = c_phi; sin_phi.at(i, j) = s_phi; } } lidar_fals.computeMInverse(cos_theta, sin_theta, cos_phi, sin_phi); } void RangeImage::createFromCloud(const pcl::PointCloud::Ptr &cloud) { createFromCloud(rows_, cols_, cloud); } void RangeImage::computeNormals(const cv::Mat &r, const cv::_OutputArray &normals_out) { // Get the normals normals_out.create(rows_, cols_, CV_MAKETYPE(depth_, 3)); cv::Mat normals = normals_out.getMat(); lidar_fals.compute(r, normals); } void RangeImage::computeNormals(const cv::Mat &r, const cv::_OutputArray &normals_out, cv::Mat &residual) { // Get the normals normals_out.create(rows_, cols_, CV_MAKETYPE(depth_, 3)); residual = cv::Mat(rows_, cols_, CV_32F, cv::Scalar::all(FLT_MAX)); cv::Mat normals = normals_out.getMat(); // cv::Mat res = residual.getMat(); lidar_fals.compute(r, normals, residual); } ================================================ FILE: src/ring_fals/range_image.h ================================================ // // Created by hk on 12/8/22. // #include #include #include #include #include #include #include "point_type.h" #include "Image_normals.hpp" #ifndef LVI_SAM_RANGE_IMAGE_H #define LVI_SAM_RANGE_IMAGE_H //in lidar frame class RangeImage { typedef pcl::PointXYZI PointType; typedef cv::Matx Mat33T; typedef cv::Vec Vec9T; typedef cv::Vec Vec3T; public: explicit RangeImage() : num_bins_x(360) , num_bins_y(360) , lidar_fals() { // range_image = cv::Mat(num_bins_y, num_bins_x, CV_32F, cv::Scalar::all(FLT_MAX)); } explicit RangeImage(const int& rows, const int& cols ) : rows_(rows) , cols_(cols) , lidar_fals(rows, cols, 3) //(row，col，window_size) { // range_image = cv::Mat(num_bins_y, num_bins_x, CV_32F, cv::Scalar::all(FLT_MAX)); //储存计算M逆矩阵的中间变量 cos_theta = cv::Mat(rows_, cols_, CV_32F); sin_theta = cv::Mat(rows_, cols_, CV_32F); cos_phi = cv::Mat(rows_, cols_, CV_32F); sin_phi = cv::Mat(rows_, cols_, CV_32F); num_per_pixel.resize(rows_ * cols_, 0); } void createFromRings(const pcl::PointCloud::Ptr& cloud); //create range imageg and build lookup table for normal computation void createAndBuildTableFromRings(const pcl::PointCloud::Ptr& cloud); void createAndBuildTableFromRings(const int& num_rings, const int& num_horizon_scan , const pcl::PointCloud::Ptr& cloud); void buildTableFromRings(const pcl::PointCloud::Ptr& cloud); void buildTableFromRings(const int& num_rings, const int& num_horizon_scan , const pcl::PointCloud::Ptr& cloud); void createFromCloud(const int& num_bins_v, const int& num_bins_h, const pcl::PointCloud::Ptr& cloud); void createFromCloud(const pcl::PointCloud::Ptr& cloud); void buildTableFromCloud(); void saveRangeImage(const std::string& file); void computeNormals(); void computeNormals(cv::OutputArray normals_out); void computeNormals(const cv::Mat &range_image, cv::OutputArray normals_out); void computeNormals(const cv::Mat &range_image, cv::OutputArray normals_out, cv::Mat &residual); void computeLookupTable(); bool loadLookupTable(std::string dir, std::string filename) {return lidar_fals.loadMInverse(dir, filename);} void saveLookupTable(std::string dir, std::string filename) {lidar_fals.saveMInverse(dir, filename);} void computeMInverse() {lidar_fals.computeMInverse(cos_theta, sin_theta, cos_phi, sin_phi);} enum CoordinateFrame { CAMERA_FRAME = 0, LASER_FRAME = 1 }; // The actual range iamge. cv::Mat range_image; //only for visualization std::vector cloud_id_image; // private: int num_bins_x, num_bins_y; float resolution_x, resolution_y; float range_min = 1.0; float range_max = 200.0; float input_value_max = 0; int downsampleRate = 1; // cv::Scalar range_init = cv::Scalar::all(FLT_MAX); int num_cloud = 0; int min_table_cloud = 10; int rows_, cols_; int depth_ = CV_32F; cv::Mat K_; int window_size_; int method_; // mutable void* rgbd_normals_impl_; LIDAR_FALS lidar_fals; bool table_valid = false; cv::Mat cos_theta; cv::Mat sin_theta; cv::Mat cos_phi; cv::Mat sin_phi; std::vector num_per_pixel; // }; #endif //LVI_SAM_RANGE_IMAGE_H ================================================ FILE: src/tic_toc.h ================================================ #pragma once #include #include #include class TicToc { public: TicToc() { tic(); } void tic() { start = std::chrono::system_clock::now(); } double toc() { end = std::chrono::system_clock::now(); std::chrono::duration elapsed_seconds = end - start; return elapsed_seconds.count() * 1000; } private: std::chrono::time_point start, end; };