[
  {
    "path": "README.md",
    "content": "# ICRA2021-SLAM-paper-list\n\nAll the classified papers can be download from Baidu cloud Disk:\nLink：https://pan.baidu.com/s/1NplHJezNTN_YetYmqI0qUg\npasswork：gban\n\n## Semantic localization and mapping：\n\n1. Visual Semantic Localization Based on HD Map for Autonomous Vehicles in Urban Scenarios\n2. RoadMap: A Light-Weight Semantic Map for Visual Localization towards Autonomous Driving\n3. Road Mapping and Localization Using Sparse Semantic Visual Features https://ieeexplore.ieee.org/document/9387091\n4. Kimera-Multi: A System for Distributed Multi-Robot Metric-Semantic Simultaneous Localization and Mapping https://arxiv.org/abs/2011.04087\n5. Semantic SLAM with Autonomous Object-Level Data Association https://arxiv.org/abs/2011.10625\n6. Hybrid Bird's-Eye Edge Based Semantic Visual SLAM for Automated Valet Parking (AVP) https://www.zhenzhenxiang.xyz/publication/icra2021/\n7. Compositional and Scalable Object SLAM https://arxiv.org/abs/2011.02658 \n8. Robust Semantic Map Matching Algorithm Based on Probabilistic Registration Model\n9. Semantically Guided Multi-View Stereo for Dense 3D Road Mapping\n10. Robust Improvement in 3D Object Landmark Inference for Semantic Mapping\n11. Any Way You Look at It: Semantic Crossview Localization and Mapping with LiDAR https://github.com/iandouglas96/cross_view_slam / https://ieeexplore.ieee.org/document/9361130\n12. PSF-LO: Parameterized Semantic Features Based Lidar Odometry https://arxiv.org/abs/2010.13355\n13. Point Set Registration with Semantic Region Association Using Cascaded Expectation Maximization\n\n\n\n## Visual SLAM\n\n#### Visual SLAM\n\n1. Asynchronous Multi-View SLAM https://arxiv.org/abs/2101.06562\n2. B-Splines for Purely Vision-Based Localization and Mapping on Non-Holonomic Ground Vehicles\n3. UPSLAM: Union of Panoramas SLAM https://arxiv.org/abs/2101.00585\n4. Multi-Parameter Optimization for a Robust RGB-D SLAM System\n5. SD-DefSLAM: Semi-Direct Monocular SLAM for Deformable and Intracorporeal Scenes https://arxiv.org/abs/2010.09409\n6. MOLTR: Multiple Object Localisation, Tracking and Reconstruction from Monocular RGB Videos https://arxiv.org/abs/2012.05360\n7. ManhattanSLAM: Robust Planar Tracking and Mapping Leveraging Mixture of Manhattan Frames https://arxiv.org/abs/2103.15068\n8. Markov Parallel Tracking and Mapping for Probabilistic SLAM\n9. Avoiding Degeneracy for Monocular Visual SLAM with Point and Line Features https://arxiv.org/abs/2103.01501\n10. Learning a State Representation and Navigation in Cluttered and Dynamic Environments https://arxiv.org/pdf/2103.04351\n11. TT-SLAM: Dense Monocular SLAM for Planar Environments https://hal.inria.fr/hal-03169199/\n12. OV2SLAM : A Fully Online and Versatile Visual SLAM for Real-Time Applications https://arxiv.org/abs/2102.04060\n13. DOT: Dynamic Object Tracking for Visual SLAM https://arxiv.org/abs/2010.00052\n14. DefSLAM: Tracking and Mapping of Deforming Scenes from Monocular Sequences (I) https://arxiv.org/abs/1908.08918\n15. RGB-D SLAM with Structural Regularities https://arxiv.org/abs/2010.07997\n16. RigidFusion: Robot Localisation and Mapping in Environments with Large Dynamic Rigid Objects\n17. CAROM - Vehicle Localization and Traffic Scene Reconstruction from Monocular Cameras on Road Infrastructures https://arxiv.org/abs/2104.00893\n18. VOLDOR-SLAM: For the Times When Feature-Based or Direct Methods Are Not Good Enough https://arxiv.org/abs/2104.06800\n19. Kimera-Multi: A System for Distributed Multi-Robot Metric-Semantic Simultaneous Localization and Mapping https://arxiv.org/abs/2011.04087\n\n#### VO:\n\n1. Accurate and Robust Scale Recovery for Monocular Visual Odometry Based on Plane Geometry https://arxiv.org/abs/2101.05995\n\n2. Accurate and Robust Stereo Direct Visual Odometry for Agricultural Environment\n\n3. Deep Online Correction for Monocular Visual Odometry https://arxiv.org/abs/2103.10029\n\n4. A Heteroscedastic Likelihood Model for Two-Frame Optical Flow https://arxiv.org/abs/2010.06871\n\n5. Learning Optical Flow with R-CNN for Visual Odometry\n\n6. Optimizing RGB-D Fusion for Accurate 6DoF Pose Estimation https://ieeexplore.ieee.org/abstract/document/9361135/\n\n7. Tight Integration of Feature-Based Relocalization in Monocular Direct Visual Odometry https://arxiv.org/abs/2102.01191\n\n8. Continuous Scale-Space Direct Image Alignment for Visual Odometry from RGB-D Images https://hal.archives-ouvertes.fr/hal-03130945/document\n\n9. A Front-End for Dense Monocular SLAM Using a Learned Outlier Mask Prior\n\n   \n\n#### Visual Mapping:\n\n1. Structure Reconstruction Using Ray-Point-Ray Features: Representation and Camera Pose Estimation\n2. Hough2Map – Iterative Event-Based Hough Transform for High-Speed Railway Mapping https://arxiv.org/pdf/2102.08145.pdf / https://github.com/ethz-asl/Hough2Map\n3. Lightweight Semantic Mesh Mapping for Autonomous Vehicles\n4. Polarimetric Monocular Dense Mapping Using Relative Deep Depth Prior https://arxiv.org/abs/2102.05212\n5. Mesh Reconstruction from Aerial Images for Outdoor Terrain Mapping Using Joint 2D-3D Learning https://arxiv.org/abs/2101.01844\n6. Direct Sparse Mapping (I)https://arxiv.org/abs/1904.06577  / https://github.com/jzubizarreta/dsm\n7. HyperMap: Compressed 3D Map for Monocular Camera Registration https://www.cs.cmu.edu/~kaess/pub/Chang21icra.pdf\n8. Probabilistic Multi-View Fusion of Active Stereo Depth Maps for Robotic Bin-Picking https://arxiv.org/abs/2103.10968\n9. Reconstructing Interactive 3D Scenes by Panoptic Mapping and CAD Model Alignments https://arxiv.org/abs/2103.16095\n\n\n\n## VIO：\n\n1. UVIP: Robust UWB Aided Visual-Inertial Positioning System for Complex Indoor Environments\n2. Range-Focused Fusion of Camera-IMU-UWB for Accurate and Drift-Reduced Localization https://ieeexplore.ieee.org/abstract/document/9350155\n3. CodeVIO: Visual-Inertial Odometry with Learned Optimizable Dense Depth https://arxiv.org/abs/2012.10133\n4. Direct Sparse Stereo Visual-Inertial Global Odometry\n5. Collaborative Visual Inertial SLAM for Multiple Smart Phones\n6. VID-Fusion: Robust Visual-Inertial-Dynamics Odometry for Accurate External Force Estimation https://arxiv.org/abs/2011.03993\n7. Run Your Visual-Inertial Odometry on NVIDIA Jetson: Benchmark Tests on a Micro Aerial Vehicle https://arxiv.org/abs/2103.01655\n8. Bidirectional Trajectory Computation for Odometer-Aided Visual-Inertial SLAM https://arxiv.org/abs/2002.00195\n9. Optimization-Based Visual-Inertial SLAM Tightly Coupled with Raw GNSS Measurements https://arxiv.org/abs/2010.11675\n10. An Equivariant Filter for Visual Inertial Odometry https://arxiv.org/abs/2104.03532\n11. Revisiting Visual-Inertial Structure-From-Motion for Odometry and SLAM Initialization https://arxiv.org/abs/2006.06017\n12. Cooperative Visual-Inertial Odometry https://hal.inria.fr/hal-02427991/document\n\n## Tracking:\n\n1. Tracking 6-DoF Object Motion from Events and Frames https://arxiv.org/abs/2103.15568\n2. Visual Tracking of Deforming Objects Using Physics-Based Models https://hal.inria.fr/hal-03179253/document\n3. Deep 6-DoF Tracking of Unknown Objects for Reactive Grasping https://arxiv.org/abs/2103.05401\n4. TSDF++: A Multi-Object Formulation for Dynamic Object Tracking and Reconstruction\n\n## Depth estimation：\n\n1. Robust Monocular Visual-Inertial Depth Completion for Embedded Systems http://udel.edu/~pgeneva/downloads/papers/c19.pdf\n2. Multimodal Scale Consistency and Awareness for Monocular Self-Supervised Depth Estimation https://arxiv.org/abs/2103.02451\n3. SelfDeco: Self-Supervised Monocular Depth Completion in Challenging Indoor Environments https://arxiv.org/abs/2011.04977\n4. Stereo-Augmented Depth Completion from a Single RGB-LiDAR Image\n5. PENet: Towards Precise and Efficient Image Guided Depth Completion https://arxiv.org/abs/2103.00783   / https://github.com/JUGGHM/PENet_ICRA2021\n6. Volumetric Propagation Network: Stereo-LiDAR Fusion for Long-Range Depth Estimation https://arxiv.org/abs/2103.12964\n7. PLG-IN: Pluggable Geometric Consistency Loss with Wasserstein Distance in Monocular Depth Estimation https://arxiv.org/abs/2006.02068\n8. Bidirectional Attention Network for Monocular Depth Estimation https://arxiv.org/abs/2009.00743\n9. Self-Guided Instance-Aware Network for Depth Completion and Enhancement\n10. Deep Multi-View Depth Estimation with Predicted Uncertainty https://arxiv.org/abs/2011.09594\n11. MultiViewStereoNet: Fast Multi-View Stereo Depth Estimation Using Incremental Viewpoint-Compensated Feature Extraction\n12. Linear Inverse Problem for Depth Completion with RGB Image and Sparse LIDAR Fusion\n13. Toward Robust and Efficient Online Adaptation for Deep Stereo Depth Estimation\n\n## Visual place recognition：\n\n1. Intelligent Reference Curation for Visual Place Recognition Via Bayesian Selective Fusion https://arxiv.org/abs/2010.09228\n2. Appearance-Based Loop Closure Detection Via Bidirectional Manifold Representation Consensus\n3. SoftMP: Attentive Feature Pooling for Joint Local Feature Detection and Description for Place Recognition in Changing Environments\n4. Simultaneous Multi-Level Descriptor Learning and Semantic Segmentation for Domain-Specific Relocalization\n5. Resolving Place Recognition Inconsistencies Using Intra-Set Similarities https://ieeexplore.ieee.org/abstract/document/9359453/\n6. Spherical Multi-Modal Place Recognition for Heterogeneous Sensor Systems https://arxiv.org/abs/2104.10067\n7. Retrieval and Localization with Observation Constraints\n8. A Flexible and Efficient Loop Closure Detection Based on Motion Knowledge\n9. Semantic Reinforced Attention Learning for Visual Place Recognition\n10. STA-VPR: Spatio-Temporal Alignment for Visual Place Recognition https://arxiv.org/abs/2103.13580\n11. Visual Place Recognition Via Local Affine Preserving Matching\n\n## lidar place recognition：\n\n1. DiSCO: Differentiable Scan Context with Orientation https://arxiv.org/abs/2010.10949\n2. Robust Place Recognition Using an Imaging Lidar https://arxiv.org/abs/2103.02111\n3. Locus: LiDAR-Based Place Recognition Using Spatiotemporal Higher-Order Pooling https://arxiv.org/abs/2011.14497\n4. Resolving Place Recognition Inconsistencies Using Intra-Set Similarities https://ieeexplore.ieee.org/document/9359453/\n5. Beyond ANN: Exploiting Structural Knowledge for Efficient Place Recognition https://arxiv.org/abs/2103.08366\n6. Place Recognition in Forests with Urquhart Tessellations http://arxiv.org/pdf/2010.03026\n\n## multi-sensor fusion localization：\n\n1. LVI-SAM: Tightly-Coupled Lidar-Visual-Inertial Odometry Via Smoothing and Mapping https://arxiv.org/abs/2104.10831 / https://github.com/TixiaoShan/LVI-SAM\n2. MSTSL: Multi-Sensor Based Two-Step Localization in Geometrically Symmetric Environments\n3. LatentSLAM: Unsupervised Multi-Sensor Representation Learning for Localization and Mapping https://arxiv.org/abs/2105.03265\n4. Visual-Laser-Inertial SLAM Using a Compact 3D Scanner for Confined Space\n5. Efficient Multi-Sensor Aided Inertial Navigation with Online Calibration\n6. Range-Visual-Inertial Odometry: Scale Observability without Excitation https://arxiv.org/pdf/2103.15215\n7. Airflow-Inertial Odometry for Resilient State Estimation on Multirotors\n8. Fusion-DHL: WiFi, IMU, and Floorplan Fusion for Dense History of Locations in Indoor Environments\n9. Interval-Based Visual-LiDAR Sensor Fusion https://www.researchgate.net/publication/349141103_Interval-Based_Visual-LiDAR_Sensor_Fusion\n10. CamVox: A Low-Cost and Accurate Lidar-Assisted Visual SLAM System https://arxiv.org/abs/2011.11357\n11. Multi-Session Underwater Pose-Graph SLAM Using Inter-Session Opti-Acoustic Two-View Factor https://irap.kaist.ac.kr/publications/hsjang-2021-icra.pdf\n12. Simple but Effective Redundant Odometry for Autonomous Vehicles https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/reinke2021icra.pdf\n13. Markov Localisation Using Heatmap Regression and Deep Convolutional Odometry https://cvssp.org/Personal/OscarMendez/papers/pdf/MendezICRA2021.pdf\n14. Unified Multi-Modal Landmark Tracking for Tightly Coupled Lidar-Visual-Inertial Odometry https://arxiv.org/abs/2011.06838\n15. Vanishing Point Aided LiDAR-Visual-Inertial Estimator https://people.inf.ethz.ch/pomarc/pubs/CamposecoICRA15.pdf\n16. Any Way You Look at It: Semantic Crossview Localization and Mapping with LiDAR https://github.com/iandouglas96/cross_view_slam / https://ieeexplore.ieee.org/document/9361130\n\n## multi-sensor fusion mapping：\n\n1. Lidar-Monocular Surface Reconstruction Using Line Segments https://arxiv.org/abs/2104.02761\n2. Automatic Mapping of Tailored Landmark Representations for Automated Driving and Map Learning\n\n## Lidar SLAM\n\n#### lidar SLAM\n\n1. SA-LOAM: Semantic-Aided LiDAR SLAM with Loop Closure\n2. Greedy-Based Feature Selection for Efficient LiDAR SLAM https://arxiv.org/abs/2103.13090\n3. Inertial Aided 3D LiDAR SLAM with Hybrid Geometric Primitives in Large-Scale Environments\n4. π-LSAM: LiDAR Smoothing and Mapping with Planes\n5. R-LOAM: Improving LiDAR Odometry and Mapping with Point-To-Mesh Features of a Known 3D Reference Object https://ieeexplore.ieee.org/document/9357902\n6. LoLa-SLAM: Low-Latency LiDAR SLAM Using Continuous Scan Slicing https://ieeexplore.ieee.org/document/9359468\n7. LiTAMIN2: Ultra Light LiDAR-Based SLAM Using Geometric Approximation Applied with KL-Divergence https://arxiv.org/abs/2103.00784\n8. 2D Laser SLAM with Closed Shape Features: Fourier Series Parameterization and Submap Joining\n9. Intensity-SLAM: Intensity Assisted Localization and Mapping for Large Scale Environment https://arxiv.org/abs/2102.03798\n10. Online Range-Based SLAM Using B-Spline Surfaces https://welcome.isr.tecnico.ulisboa.pt/wp-content/uploads/2021/03/09359349.pdf\n11. MULLS: Versatile LiDAR SLAM Via Multi-Metric Linear Least Square https://arxiv.org/abs/2102.03771 / https://github.com/YuePanEdward/MULLS\n12. Dynamic Object Aware LiDAR SLAM Based on Automatic Generation of Training Data https://arxiv.org/abs/2104.03657\n13. A FastSLAM Approach Integrating Beamforming Maps for Ultrasound-Based Robotic Inspection of Metal Structures https://hal.archives-ouvertes.fr/hal-03017841/document\n\n#### lidar localization:\n\n1. Robust LiDAR Feature Localization for Autonomous Vehicles Using Geometric Fingerprinting on Open Datasets https://github.com/dcmlr/fingerprint-localization / https://ieeexplore.ieee.org/abstract/document/9363614/\n2. Robust SRIF-Based LiDAR-IMU Localization for Autonomous Vehicles\n3. NDT-Transformer: Large-Scale 3D Point Cloud Localisation Using the Normal Distribution Transform Representation https://arxiv.org/abs/2103.12292\n4. Connecting Semantic Building Information Models and Robotics: An Application to 2D LiDAR-Based Localization\n\n#### lidar mapping:\n\n1. BALM: Bundle Adjustment for Lidar Mapping https://arxiv.org/pdf/2010.08215\n2. Accelerating Probabilistic Volumetric Mapping Using Ray-Tracing Graphics Hardware https://arxiv.org/abs/2011.10348\n3. ERASOR: Egocentric Ratio of Pseudo Occupancy-Based Dynamic Object Removal for Static 3D Point Cloud Map Building https://arxiv.org/abs/2103.04316\n4. Multiresolution Representations for Large-Scale Terrain with Local Gaussian Process Regression\n5. Kernel-Based 3-D Dynamic Occupancy Mapping with Particle Tracking\n6. Poisson Surface Reconstruction for LiDAR Odometry and Mapping  https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/vizzo2021icra.pdf\n7. Dynamic Occupancy Grid Mapping with Recurrent Neural Networks https://arxiv.org/abs/2011.08659\n8. Semantic Mapping of Construction Site from Multiple Daily Airborne LiDAR Data https://ieeexplore.ieee.org/document/9364688/\n9. Multi-Resolution 3D Mapping with Explicit Free Space Representation for Fast and Accurate Mobile Robot Motion Planning https://arxiv.org/abs/2010.07929\n10. MCMC Occupancy Grid Mapping with a Data-Driven Patch Prior\n11. Elastic and Efficient LiDAR Reconstruction for Large-Scale Exploration Tasks https://arxiv.org/abs/2010.09232\n\n#### LO & LIO:\n\n1. FAST-LIO: A Fast, Robust LiDAR-Inertial Odometry Package by Tightly-Coupled Iterated Kalman Filter https://arxiv.org/abs/2010.08196\n2. KFS-LIO: Key-Feature Selection for Lightweight Lidar Inertial Odometry\n3. LIRO: Tightly Coupled Lidar-Inertia-Ranging Odometry https://arxiv.org/abs/2010.13072\n4. PSF-LO: Parameterized Semantic Features Based Lidar Odometry https://arxiv.org/abs/2010.13355\n5. ENCODE: A dEep poiNt Cloud ODometry NEtwork\n6. Automatic Hyper-Parameter Tuning for Black-Box LiDAR Odometry\n7. Self-Supervised Learning of LiDAR Odometry for Robotic Applications https://arxiv.org/pdf/2011.05418\n\n#### Point Cloud Registration:\n\n1. PHASER: A Robust and Correspondence-Free Global Pointcloud Registration https://arxiv.org/abs/2102.02767\n2.  Differential Information Aided 3-D Registration for Accurate Navigation and Scene Reconstruction https://www.researchgate.net/publication/349678605_Differential_Information_Aided_3-D_Registration_for_Accurate_Navigation_and_Scene_Reconstruction/link/603be223299bf1cc26fbc4c3/download\n3. Robust Motion Averaging under Maximum Correntropy Criterion https://arxiv.org/pdf/2004.09829\n4. Toward a Unified Framework for Point Set Registration http://cvl.ist.osaka-u.ac.jp/wp-content/uploads/2021/03/li_icra2021.pdf\n5. Voxelized GICP for Fast and Accurate 3D Point Cloud Registration https://easychair.org/publications/preprint/ftvV\n6. Probabilistic Scan Matching: Bayesian Pose Estimation from Point Clouds\n7. Learning the Next Best View for 3D Point Clouds Via Topological Features https://arxiv.org/abs/2103.02789\n8. A New Framework for Registration of Semantic Point Clouds from Stereo and RGB-D Cameras https://arxiv.org/abs/2012.03683\n\n#### feature:\n\n1. SKD: Keypoint Detection for Point Clouds Using Saliency Estimation https://arxiv.org/abs/1912.04943\n2. Unsupervised Learning of Lidar Features for Use in a Probabilistic Trajectory Estimator https://arxiv.org/pdf/2102.11261\n\n\n\n#### Solid-state lidar：\n\n1. Lightweight 3-D Localization and Mapping for Solid-State LiDAR https://arxiv.org/abs/2102.03800 \n\n#### Point cloud compression：\n\n1. Deep Compression for Dense Point Cloud Maps https://github.com/PRBonn/deep-point-map-compression / https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/wiesmann2021ral.pdf\n\n## Global localization：\n\n1. Robust LiDAR Feature Localization for Autonomous Vehicles Using Geometric Fingerprinting on Open Datasets https://github.com/dcmlr/fingerprint-localization / https://ieeexplore.ieee.org/document/9363614\n2. Learned Uncertainty Calibration for Visual Inertial Localization\n3. Deep Samplable Observation Model for Global Localization and Kidnapping https://arxiv.org/abs/2009.00211\n4. Camera Relocalization Using Deep Point Cloud Generation and Hand-Crafted Feature Refinement\n5. Semantic Histogram Based Graph Matching for Real-Time Multi-Robot Global Localization in Large Scale Environment https://arxiv.org/abs/2010.09297\n6. LiDAR-Based Initial Global Localization Using Two-Dimensional (2D) Submap Projection Image (SPI)\n7. Global Aerial Localisation Using Image and Map Embeddings\n8. Range Image-Based LiDAR Localization for Autonomous Vehicles https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/chen2021icra.pdf\n9. RadarLoc: Learning to Relocalize in FMCW Radar https://arxiv.org/abs/2103.11562\n10. Freetures: Localization in Signed Distance Function Maps https://arxiv.org/abs/2010.09378\n11. Self-Supervised Learning of Domain-Invariant Local Features for Robust Visual Localization under Challenging Conditions https://ieeexplore.ieee.org/abstract/document/9354898/\n12. Learning to Localize in New Environments from Synthetic Training Data https://arxiv.org/abs/2011.04539\n13. Tightly-Coupled Multi-Sensor Fusion for Localization with LiDAR Feature Maps\n14. Robust Dual Quadric Initialization for Forward-Translating Camera Movements https://ieeexplore.ieee.org/document/9384189\n15. 3D Surfel Map-Aided Visual Relocalization with Learned Descriptors https://arxiv.org/abs/2104.03856\n\n\n\n## Learning-based：\n\n1. End-To-End Semi-Supervised Learning for Differentiable Particle Filters https://arxiv.org/abs/2011.05748\n2. Initialisation of Autonomous Aircraft Visual Inspection Systems Via CNN-Based Camera Pose Estimation\n\n\n\n## Radar：\n\n1. Do We Need to Compensate for Motion Distortion and Doppler Effects in Spinning Radar Navigation? https://github.com/keenan-burnett/yeti_radar_odometry / https://www.semanticscholar.org/paper/Do-We-Need-to-Compensate-for-Motion-Distortion-and-Burnett-Schoellig/1f20dab73a7e04c4f8dc801bd1de104b808a07db / https://arxiv.org/pdf/2011.03512\n2. RadarLoc: Learning to Relocalize in FMCW Radar https://arxiv.org/abs/2103.11562\n3. A Normal Distribution Transform-Based Radar Odometry Designed for Scanning and Automotive Radars\n\n\n## Data association：\n\n1. CLEAR: A Consistent Lifting, Embedding, and Alignment Rectification Algorithm for Multiview Data Association (I) https://arxiv.org/abs/1902.02256\n2. ROBIN: A Graph-Theoretic Approach to Reject Outliers in Robust Estimation Using Invariants https://arxiv.org/abs/2011.03659\n3. CLIPPER: A Graph-Theoretic Framework for Robust Data Association https://arxiv.org/abs/2011.10202\n\n\n\n## Back-end：\n\n1. NF-iSAM: Incremental Smoothing and Mapping Via Normalizing Flows\n2. A Switching-Coupled Backend for Simultaneous Localization and Dynamic Object Tracking\n\n\n\n## Distributed SLAM：\n\n1. Distributed Client-Server Optimization for SLAM with Limited On-Device Resources https://arxiv.org/abs/2103.14303\n2. Invariant Extended Kalman Filtering Using Two Position Receivers for Extended Pose Estimation https://arxiv.org/abs/2104.14711\n3. Compartmentalized Covariance Intersection: A Novel Filter Architecture for Distributed Localization\n4. Towards Robust State Estimation by Boosting the Maximum Correntropy Criterion Kalman Filter with Adaptive Behaviors https://arxiv.org/abs/2103.15354\n5. Vehicle-To-Vehicle Collaborative Graph-Based Proprioceptive Localization https://scholar.google.com/scholar?oi=bibs&hl=es&cluster=16177649896940716005\n\n## long-term:\n\n1. Lifelong Localization in Semi-Dynamic Environment\n\n## Calibration：\n\n1. Extrinsic Calibration of Multiple LiDARs of Small FoV in Targetless Environments http://link.zhihu.com/?target=https%3A//ieeexplore.ieee.org/document/9361153\n2. Efficient Online Calibration for Autonomous Vehicle's Longitudinal Dynamical System: A Gaussian Model Approach\n3. Automated Extrinsic Calibration for 3D LiDARs with Range Offset Correction Using an Arbitrary Planar Board\n4. Targetless Multiple Camera-LiDAR Extrinsic Calibration Using Object Pose Estimation\n5. Online Photometric Calibration of Automatic Gain Thermal Infrared Cameras https://arxiv.org/abs/2012.14292\n6. A Continuous-Time Approach for 3D Radar to Camera Extrinsic Calibration https://arxiv.org/abs/2103.07505\n7. Learned Camera Gain and Exposure Control for Improved Visual Feature Detection and Matching https://arxiv.org/abs/2102.04341\n8. Auto-Calibration Method Using Stop Signs for Urban Autonomous Driving Applications https://arxiv.org/abs/2010.07441\n\n\n\n## UWB：\n\n1. Bias Compensated UWB Anchor Initialization Using Information-Theoretic Supported Triangulation Points https://www.aau.at/wp-content/uploads/2021/03/UWB_Initialization_ICRA_CNS.pdf\n2. Relative Position Estimation between Two UWB Devices with IMUs https://arxiv.org/abs/2104.10730\n3. UWB Indoor Global Localisation for Nonholonomic Robots with Unknown Offset Compensation\n4. Consistent State Estimation on Manifolds for Autonomous Metal Structure Inspection https://www.researchgate.net/profile/Alessandro-Fornasier/publication/350459466_Consistent_State_Estimation_on_Manifolds_for_Autonomous_Metal_Structure_Inspection/links/6061b364a6fdccbfea147687/Consistent-State-Estimation-on-Manifolds-for-Autonomous-Metal-Structure-Inspection.pdf\n\n\n\n## Math related：\n\n1. Efficient Modification of the Upper Triangular Square Root Matrix on Variable Reordering https://www.researchgate.net/publication/347950562_Efficient_Modification_of_the_Upper_Triangular_Square_Root_Matrix_on_Variable_Reordering/link/6009d60a92851c13fe2a8084/download\n2. Robust 360-8PA: Redesigning the Normalized 8-Point Algorithm for 360-FoV Images https://arxiv.org/abs/2104.10900\n\n\n\n## Dataset：\n\n1. RADIATE: A Radar Dataset for Automotive Perception in Bad Weather https://arxiv.org/pdf/2010.09076\n2. Cirrus: A Long-Range Bi-Pattern LiDAR Dataset https://arxiv.org/abs/2012.02938\n3. VIODE: A Simulated Dataset to Address the Challenges of Visual-Inertial Odometry in Dynamic Environments https://github.com/kminoda/VIODE / https://www.semanticscholar.org/paper/VIODE%3A-A-Simulated-Dataset-to-Address-the-of-in-Minoda-Schilling/2f339961731cbaedf54d71f874541a5894ef5a15\n4. A Multi-Spectral Dataset for Evaluating Motion Estimation Systems https://arxiv.org/abs/2007.00622\n5. PicoVO: A Lightweight RGB-D Visual Odometry Targeting Resource-Constrained IoT Devices\n6. Are We Ready for Unmanned Surface Vehicles in Inland Waterways? the USVInland Multisensor Dataset and Benchmark https://arxiv.org/abs/2103.05383\n7. DSEC: A Stereo Event Camera Dataset for Driving Scenarios https://arxiv.org/abs/2103.06011\n8. AcinoSet: A 3D Pose Estimation Dataset and Baseline Models for Cheetahs in the Wild https://github.com/African-Robotics-Unit/AcinoSet / https://arxiv.org/abs/2103.13282\n\n\n\n## Medical localization：\n\n1. Robotically Surgical Vessel Localization Using Robust Hybrid Video Motion Magnification https://ieeexplore.ieee.org/document/9353981\n\n## Sound source localization：\n\n1. GCC-PHAT with Speech-Oriented Attention for Robotic Sound Source Localization\n\n## GNSS：\n\n1. Towards Robust GNSS Positioning and Real-Time Kinematic Using Factor Graph Optimization\n\n## UAV：\n\n1. Tracking and Relative Localization of Drone Swarms with a Vision-Based Headset https://ieeexplore.ieee.org/document/9324934\n2. UAV Localization Using Autoencoded Satellite Images https://arxiv.org/abs/2102.05692\n3. Learning-Based Bias Correction for Time Difference of Arrival Ultra-Wideband Localization of Resource-Constrained Mobile Robots https://arxiv.org/abs/2103.01885\n4. Sensing Via Collisions: A Smart Cage for Quadrotors with Applications to Self-Localization\n\n\n\n## Robotics localization：\n\n1. Improving Ranging-Based Location Estimation with Rigidity-Constrained CRLB-Based Motion Planning\n2. Relative Position Estimation in Multi-Agent Systems Using Attitude-Coupled Range Measurements\n3. Rover Relocalization for Mars Sample Return by Virtual Template Synthesis and Matching https://arxiv.org/abs/2103.03395\n4. A Comparison between Joint Space and Task Space Mappings for Dynamic Teleoperation of an Anthropomorphic Robotic Arm in Reaction Tests https://arxiv.org/abs/2011.02508\n5. State Estimation for Hybrid Wheeled-Legged Robots Performing Mobile Manipulation Tasks\n6. Robust Localization for Planar Moving Robot in Changing Environment: A Perspective on Density of Correspondence and Depth https://arxiv.org/abs/2011.00439\n\n\n\n## PPA：\n\n1. Weighted Node Mapping and Localisation on a Pixel Processor Array https://www.researchgate.net/publication/350187131_Weighted_Node_Mapping_and_Localisation_on_a_Pixel_Processor_Array/link/6054d443299bf17367550a00/download\n\n## Sonar:\n\n1. Predictive 3D Sonar Mapping of Underwater Environments Via Object-Specific Bayesian Inference https://arxiv.org/abs/2104.03203\n\n## Tactile SLAM：\n\n1. Tactile SLAM: Real-Time Inference of Shape and Pose from Planar Pushing https://arxiv.org/abs/2011.07044\n\n## Active SLAM：\n\n1. Invariant EKF Based 2D Active SLAM with Exploration Task\n\n## IMU：\n\n1. IMU Data Processing for Inertial Aided Navigation: A Recurrent Neural Network Based Approach https://arxiv.org/abs/2103.14286\n2. Highly Efficient Line Segment Tracking with an IMU-KLT Prediction and a Convex Geometric Distance Minimization\n3. IMU/Vehicle Calibration and Integrated Localization for Autonomous Driving\n4. Reinforcement Learning for Orientation Estimation Using Inertial Sensors with Performance Guarantee https://arxiv.org/abs/2103.02357\n"
  }
]