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Repository: JoestarK/LiDAR-Iris
Branch: master
Commit: 168ea358c9a2
Files: 13
Total size: 12.4 MB
Directory structure:
gitextract_wz8f4grx/
├── CMakeLists.txt
├── LidarIris/
│ ├── LidarIris.cpp
│ ├── LidarIris.h
│ └── fftm/
│ ├── fftm.cpp
│ └── fftm.hpp
├── README.md
├── data/
│ ├── 16line.pcd
│ ├── 16line_1.pcd
│ ├── 16line_2.pcd
│ ├── 64line.pcd
│ ├── 64line_1.pcd
│ └── 64line_2.pcd
└── demo.cpp
================================================
FILE CONTENTS
================================================
================================================
FILE: CMakeLists.txt
================================================
cmake_minimum_required (VERSION 3.8)
SET(CMAKE_BUILD_TYPE Release)
set(CMAKE_PREFIX_PATH ${CMAKE_PREFIX_PATH} "/usr/lib/x86_64-linux-gnu/cmake")
set(OpenCV_DIR "/usr/local/share/OpenCV")
project (LidarIris)
find_package(PCL REQUIRED)
find_package(OpenCV REQUIRED)
include_directories(${PCL_INCLUDE_DIRS} ${OpenCV_INCLUDE_DIRS})
link_directories(${PCL_LIBRARY_DIRS})
add_definitions(${PCL_DEFINITIONS})
include_directories("./LidarIris/")
add_executable(demo "demo.cpp" "LidarIris/LidarIris.cpp" "LidarIris/LidarIris.h" "LidarIris/fftm/fftm.cpp" "LidarIris/fftm/fftm.hpp")
target_link_libraries(demo ${PCL_LIBRARIES} ${OpenCV_LIBS})
================================================
FILE: LidarIris/LidarIris.cpp
================================================
#include "LidarIris.h"
#include "fftm/fftm.hpp"
cv::Mat1b LidarIris::GetIris(const pcl::PointCloud<pcl::PointXYZ> &cloud)
{
cv::Mat1b IrisMap = cv::Mat1b::zeros(80, 360);
// 16-line
// for (pcl::PointXYZ p : cloud.points)
// {
// float dis = sqrt(p.data[0] * p.data[0] + p.data[1] * p.data[1]);
// float arc = (atan2(p.data[2], dis) * 180.0f / M_PI) + 15;
// float yaw = (atan2(p.data[1], p.data[0]) * 180.0f / M_PI) + 180;
// int Q_dis = std::min(std::max((int)floor(dis), 0), 79);
// int Q_arc = std::min(std::max((int)floor(arc / 4.0f), 0), 7);
// int Q_yaw = std::min(std::max((int)floor(yaw + 0.5), 0), 359);
// IrisMap.at<uint8_t>(Q_dis, Q_yaw) |= (1 << Q_arc);
// }
// 64-line
for (pcl::PointXYZ p : cloud.points)
{
float dis = sqrt(p.data[0] * p.data[0] + p.data[1] * p.data[1]);
float arc = (atan2(p.data[2], dis) * 180.0f / M_PI) + 24;
float yaw = (atan2(p.data[1], p.data[0]) * 180.0f / M_PI) + 180;
int Q_dis = std::min(std::max((int)floor(dis), 0), 79);
int Q_arc = std::min(std::max((int)floor(arc / 4.0f), 0), 7);
int Q_yaw = std::min(std::max((int)floor(yaw + 0.5), 0), 359);
IrisMap.at<uint8_t>(Q_dis, Q_yaw) |= (1 << Q_arc);
}
return IrisMap;
}
void LidarIris::UpdateFrame(const cv::Mat1b &frame, int frameIndex, float *matchDistance, int *matchIndex)
{
// first: calc feature
std::vector<float> vec;
auto feature = GetFeature(frame, vec);
flann::Matrix<float> queries(vec.data(), 1, vec.size());
if (featureList.size() == 0)
{
if (matchDistance)
*matchDistance = NAN;
if (matchIndex)
*matchIndex = -1;
vecList.buildIndex(queries);
}
else
{
// second: search in database
vecList.knnSearch(queries, indices, dists, _matchNum, flann::SearchParams(32));
//thrid: calc matches
std::vector<float> dis(_matchNum);
for (int i = 0; i < _matchNum; i++)
{
dis[i] = Compare(feature, featureList[indices[0][i]]);
}
int minIndex = std::min_element(dis.begin(), dis.end()) - dis.begin();
if (matchDistance)
*matchDistance = dis[minIndex];
if (matchIndex)
*matchIndex = frameIndexList[indices[0][minIndex]];
// forth: add frame to database
vecList.addPoints(queries);
}
featureList.push_back(feature);
frameIndexList.push_back(frameIndex);
}
float LidarIris::Compare(const LidarIris::FeatureDesc &img1, const LidarIris::FeatureDesc &img2, int *bias)
{
auto firstRect = FFTMatch(img2.img, img1.img);
int firstShift = firstRect.center.x - img1.img.cols / 2;
float dis1;
int bias1;
GetHammingDistance(img1.T, img1.M, img2.T, img2.M, firstShift, dis1, bias1);
//
auto T2x = circShift(img2.T, 0, 180);
auto M2x = circShift(img2.M, 0, 180);
auto img2x = circShift(img2.img, 0, 180);
//
auto secondRect = FFTMatch(img2x, img1.img);
int secondShift = secondRect.center.x - img1.img.cols / 2;
float dis2 = 0;
int bias2 = 0;
GetHammingDistance(img1.T, img1.M, T2x, M2x, secondShift, dis2, bias2);
//
if (dis1 < dis2)
{
if (bias)
*bias = bias1;
return dis1;
}
else
{
if (bias)
*bias = (bias2 + 180) % 360;
return dis2;
}
}
std::vector<cv::Mat2f> LidarIris::LogGaborFilter(const cv::Mat1f &src, unsigned int nscale, int minWaveLength, double mult, double sigmaOnf)
{
int rows = src.rows;
int cols = src.cols;
cv::Mat2f filtersum = cv::Mat2f::zeros(1, cols);
std::vector<cv::Mat2f> EO(nscale);
int ndata = cols;
if (ndata % 2 == 1)
ndata--;
cv::Mat1f logGabor = cv::Mat1f::zeros(1, ndata);
cv::Mat2f result = cv::Mat2f::zeros(rows, ndata);
cv::Mat1f radius = cv::Mat1f::zeros(1, ndata / 2 + 1);
radius.at<float>(0, 0) = 1;
for (int i = 1; i < ndata / 2 + 1; i++)
{
radius.at<float>(0, i) = i / (float)ndata;
}
double wavelength = minWaveLength;
for (int s = 0; s < nscale; s++)
{
double fo = 1.0 / wavelength;
double rfo = fo / 0.5;
//
cv::Mat1f temp; //(radius.size());
cv::log(radius / fo, temp);
cv::pow(temp, 2, temp);
cv::exp((-temp) / (2 * log(sigmaOnf) * log(sigmaOnf)), temp);
temp.copyTo(logGabor.colRange(0, ndata / 2 + 1));
//
logGabor.at<float>(0, 0) = 0;
cv::Mat2f filter;
cv::Mat1f filterArr[2] = {logGabor, cv::Mat1f::zeros(logGabor.size())};
cv::merge(filterArr, 2, filter);
filtersum = filtersum + filter;
for (int r = 0; r < rows; r++)
{
cv::Mat2f src2f;
cv::Mat1f srcArr[2] = {src.row(r).clone(), cv::Mat1f::zeros(1, src.cols)};
cv::merge(srcArr, 2, src2f);
cv::dft(src2f, src2f);
cv::mulSpectrums(src2f, filter, src2f, 0);
cv::idft(src2f, src2f);
src2f.copyTo(result.row(r));
}
EO[s] = result.clone();
wavelength *= mult;
}
filtersum = circShift(filtersum, 0, cols / 2);
return EO;
}
void LidarIris::LoGFeatureEncode(const cv::Mat1b &src, unsigned int nscale, int minWaveLength, double mult, double sigmaOnf, cv::Mat1b &T, cv::Mat1b &M)
{
cv::Mat1f srcFloat;
src.convertTo(srcFloat, CV_32FC1);
auto list = LogGaborFilter(srcFloat, nscale, minWaveLength, mult, sigmaOnf);
std::vector<cv::Mat1b> Tlist(nscale * 2), Mlist(nscale * 2);
for (int i = 0; i < list.size(); i++)
{
cv::Mat1f arr[2];
cv::split(list[i], arr);
Tlist[i] = arr[0] > 0;
Tlist[i + nscale] = arr[1] > 0;
cv::Mat1f m;
cv::magnitude(arr[0], arr[1], m);
Mlist[i] = m < 0.0001;
Mlist[i + nscale] = m < 0.0001;
}
cv::vconcat(Tlist, T);
cv::vconcat(Mlist, M);
}
LidarIris::FeatureDesc LidarIris::GetFeature(const cv::Mat1b &src)
{
FeatureDesc desc;
desc.img = src;
LoGFeatureEncode(src, _nscale, _minWaveLength, _mult, _sigmaOnf, desc.T, desc.M);
return desc;
}
LidarIris::FeatureDesc LidarIris::GetFeature(const cv::Mat1b &src, std::vector<float> &vec)
{
cv::Mat1f temp;
src.convertTo(temp, CV_32FC1);
cv::reduce((temp != 0) / 255, temp, 1, CV_REDUCE_AVG);
vec = temp.isContinuous() ? temp : temp.clone();
return GetFeature(src);
}
void LidarIris::GetHammingDistance(const cv::Mat1b &T1, const cv::Mat1b &M1, const cv::Mat1b &T2, const cv::Mat1b &M2, int scale, float &dis, int &bias)
{
dis = NAN;
bias = -1;
for (int shift = scale - 2; shift <= scale + 2; shift++)
{
cv::Mat1b T1s = circShift(T1, 0, shift);
cv::Mat1b M1s = circShift(M1, 0, shift);
cv::Mat1b mask = M1s | M2;
int MaskBitsNum = cv::sum(mask / 255)[0];
int totalBits = T1s.rows * T1s.cols - MaskBitsNum;
cv::Mat1b C = T1s ^ T2;
C = C & ~mask;
int bitsDiff = cv::sum(C / 255)[0];
if (totalBits == 0)
{
dis = NAN;
}
else
{
float currentDis = bitsDiff / (float)totalBits;
if (currentDis < dis || isnan(dis))
{
dis = currentDis;
bias = shift;
}
}
}
return;
}
inline cv::Mat LidarIris::circRowShift(const cv::Mat &src, int shift_m_rows)
{
if (shift_m_rows == 0)
return src.clone();
shift_m_rows %= src.rows;
int m = shift_m_rows > 0 ? shift_m_rows : src.rows + shift_m_rows;
cv::Mat dst(src.size(), src.type());
src(cv::Range(src.rows - m, src.rows), cv::Range::all()).copyTo(dst(cv::Range(0, m), cv::Range::all()));
src(cv::Range(0, src.rows - m), cv::Range::all()).copyTo(dst(cv::Range(m, src.rows), cv::Range::all()));
return dst;
}
inline cv::Mat LidarIris::circColShift(const cv::Mat &src, int shift_n_cols)
{
if (shift_n_cols == 0)
return src.clone();
shift_n_cols %= src.cols;
int n = shift_n_cols > 0 ? shift_n_cols : src.cols + shift_n_cols;
cv::Mat dst(src.size(), src.type());
src(cv::Range::all(), cv::Range(src.cols - n, src.cols)).copyTo(dst(cv::Range::all(), cv::Range(0, n)));
src(cv::Range::all(), cv::Range(0, src.cols - n)).copyTo(dst(cv::Range::all(), cv::Range(n, src.cols)));
return dst;
}
cv::Mat LidarIris::circShift(const cv::Mat &src, int shift_m_rows, int shift_n_cols)
{
return circColShift(circRowShift(src, shift_m_rows), shift_n_cols);
}
================================================
FILE: LidarIris/LidarIris.h
================================================
#ifndef _LIDAR_IRIS_H_
#define _LIDAR_IRIS_H_
#include <vector>
#include <flann/flann.hpp>
#include <opencv2/opencv.hpp>
#include <pcl/point_types.h>
#include <pcl/point_cloud.h>
class LidarIris
{
public:
struct FeatureDesc
{
cv::Mat1b img;
cv::Mat1b T;
cv::Mat1b M;
};
LidarIris(int nscale, int minWaveLength, float mult, float sigmaOnf, int matchNum) : _nscale(nscale),
_minWaveLength(minWaveLength),
_mult(mult),
_sigmaOnf(sigmaOnf),
_matchNum(matchNum),
vecList(flann::Index<flann::L2<float>>(flann::KDTreeIndexParams(4))),
// indicesBuffer(std::vector<int>(matchNum)),
// distsBuffer(std::vector<float>(matchNum)),
indices(flann::Matrix<int>(new int[matchNum], 1, matchNum)),
dists(flann::Matrix<float>(new float[matchNum], 1, matchNum))
{
}
LidarIris(const LidarIris &) = delete;
LidarIris &operator=(const LidarIris &) = delete;
static cv::Mat1b GetIris(const pcl::PointCloud<pcl::PointXYZ> &cloud);
//
void UpdateFrame(const cv::Mat1b &frame, int frameIndex, float *matchDistance, int *matchIndex);
//
float Compare(const FeatureDesc &img1, const FeatureDesc &img2, int *bias = nullptr);
//
FeatureDesc GetFeature(const cv::Mat1b &src);
FeatureDesc GetFeature(const cv::Mat1b &src, std::vector<float> &vec);
std::vector<cv::Mat2f> LogGaborFilter(const cv::Mat1f &src, unsigned int nscale, int minWaveLength, double mult, double sigmaOnf);
void GetHammingDistance(const cv::Mat1b &T1, const cv::Mat1b &M1, const cv::Mat1b &T2, const cv::Mat1b &M2, int scale, float &dis, int &bias);
//
static inline cv::Mat circRowShift(const cv::Mat &src, int shift_m_rows);
static inline cv::Mat circColShift(const cv::Mat &src, int shift_n_cols);
static cv::Mat circShift(const cv::Mat &src, int shift_m_rows, int shift_n_cols);
private:
void LoGFeatureEncode(const cv::Mat1b &src, unsigned int nscale, int minWaveLength, double mult, double sigmaOnf, cv::Mat1b &T, cv::Mat1b &M);
int _nscale;
int _minWaveLength;
float _mult;
float _sigmaOnf;
int _matchNum;
flann::Index<flann::L2<float>> vecList;
std::vector<FeatureDesc> featureList;
std::vector<int> frameIndexList;
flann::Matrix<int> indices;
flann::Matrix<float> dists;
// std::vector<int> indicesBuffer;
// std::vector<float> distsBuffer;
};
#endif
================================================
FILE: LidarIris/fftm/fftm.cpp
================================================
#include "opencv2/core.hpp"
#include "opencv2/opencv.hpp"
using namespace std;
using namespace cv;
//----------------------------------------------------------
// Recombinate image quaters
//----------------------------------------------------------
void Recomb(Mat &src, Mat &dst)
{
int cx = src.cols >> 1;
int cy = src.rows >> 1;
Mat tmp;
tmp.create(src.size(), src.type());
src(Rect(0, 0, cx, cy)).copyTo(tmp(Rect(cx, cy, cx, cy)));
src(Rect(cx, cy, cx, cy)).copyTo(tmp(Rect(0, 0, cx, cy)));
src(Rect(cx, 0, cx, cy)).copyTo(tmp(Rect(0, cy, cx, cy)));
src(Rect(0, cy, cx, cy)).copyTo(tmp(Rect(cx, 0, cx, cy)));
dst = tmp;
}
//----------------------------------------------------------
// 2D Forward FFT
//----------------------------------------------------------
void ForwardFFT(Mat &Src, Mat *FImg, bool do_recomb = true)
{
int M = getOptimalDFTSize(Src.rows);
int N = getOptimalDFTSize(Src.cols);
Mat padded;
copyMakeBorder(Src, padded, 0, M - Src.rows, 0, N - Src.cols, BORDER_CONSTANT, Scalar::all(0));
Mat planes[] = { Mat_<float>(padded), Mat::zeros(padded.size(), CV_32F) };
Mat complexImg;
merge(planes, 2, complexImg);
dft(complexImg, complexImg);
split(complexImg, planes);
planes[0] = planes[0](Rect(0, 0, planes[0].cols & -2, planes[0].rows & -2));
planes[1] = planes[1](Rect(0, 0, planes[1].cols & -2, planes[1].rows & -2));
if (do_recomb)
{
Recomb(planes[0], planes[0]);
Recomb(planes[1], planes[1]);
}
planes[0] /= float(M*N);
planes[1] /= float(M*N);
FImg[0] = planes[0].clone();
FImg[1] = planes[1].clone();
}
//----------------------------------------------------------
// 2D inverse FFT
//----------------------------------------------------------
void InverseFFT(Mat *FImg, Mat &Dst, bool do_recomb = true)
{
if (do_recomb)
{
Recomb(FImg[0], FImg[0]);
Recomb(FImg[1], FImg[1]);
}
Mat complexImg;
merge(FImg, 2, complexImg);
idft(complexImg, complexImg);
split(complexImg, FImg);
Dst = FImg[0].clone();
}
//-----------------------------------------------------------------------------------------------------
//
//-----------------------------------------------------------------------------------------------------
void highpass(Size sz, Mat& dst)
{
Mat a = Mat(sz.height, 1, CV_32FC1);
Mat b = Mat(1, sz.width, CV_32FC1);
float step_y = CV_PI / sz.height;
float val = -CV_PI*0.5;
for (int i = 0; i < sz.height; ++i)
{
a.at<float>(i) = cos(val);
val += step_y;
}
val = -CV_PI*0.5;
float step_x = CV_PI / sz.width;
for (int i = 0; i < sz.width; ++i)
{
b.at<float>(i) = cos(val);
val += step_x;
}
Mat tmp = a*b;
dst = (1.0 - tmp).mul(2.0 - tmp);
}
//-----------------------------------------------------------------------------------------------------
//
//-----------------------------------------------------------------------------------------------------
float logpolar(Mat& src, Mat& dst)
{
float radii = src.cols;
float angles = src.rows;
Point2f center(src.cols / 2, src.rows / 2);
float d = norm(Vec2f(src.cols - center.x, src.rows - center.y));
float log_base = pow(10.0, log10(d) / radii);
float d_theta = CV_PI / (float)angles;
float theta = CV_PI / 2.0;
float radius = 0;
Mat map_x(src.size(), CV_32FC1);
Mat map_y(src.size(), CV_32FC1);
for (int i = 0; i < angles; ++i)
{
for (int j = 0; j < radii; ++j)
{
radius = pow(log_base, float(j));
float x = radius * sin(theta) + center.x;
float y = radius * cos(theta) + center.y;
map_x.at<float>(i, j) = x;
map_y.at<float>(i, j) = y;
}
theta += d_theta;
}
remap(src, dst, map_x, map_y, CV_INTER_LINEAR, BORDER_CONSTANT, Scalar(0, 0, 0));
return log_base;
}
//-----------------------------------------------------------------------------------------------------
// As input we need equal sized images, with the same aspect ratio,
// scale difference should not exceed 1.8 times.
//-----------------------------------------------------------------------------------------------------
RotatedRect LogPolarFFTTemplateMatch(Mat& im0, Mat& im1/*, double canny_threshold1, double canny_threshold2*/)
{
// Accept 1 or 3 channel CV_8U, CV_32F or CV_64F images.
CV_Assert((im0.type() == CV_8UC1) || (im0.type() == CV_8UC3) ||
(im0.type() == CV_32FC1) || (im0.type() == CV_32FC3) ||
(im0.type() == CV_64FC1) || (im0.type() == CV_64FC3));
CV_Assert(im0.rows == im1.rows && im0.cols == im1.cols);
CV_Assert(im0.channels() == 1 || im0.channels() == 3 || im0.channels() == 4);
CV_Assert(im1.channels() == 1 || im1.channels() == 3 || im1.channels() == 4);
//Mat im0_tmp = im0.clone();
//Mat im1_tmp = im1.clone();
if (im0.channels() == 3)
{
cvtColor(im0, im0, cv::COLOR_BGR2GRAY);
}
if (im0.channels() == 4)
{
cvtColor(im0, im0, cv::COLOR_BGRA2GRAY);
}
if (im1.channels() == 3)
{
cvtColor(im1, im1, cv::COLOR_BGR2GRAY);
}
if (im1.channels() == 4)
{
cvtColor(im1, im1, cv::COLOR_BGRA2GRAY);
}
if (im0.type() == CV_32FC1)
{
im0.convertTo(im0, CV_8UC1, 255.0);
}
if (im1.type() == CV_32FC1)
{
im1.convertTo(im1, CV_8UC1, 255.0);
}
if (im0.type() == CV_64FC1)
{
im0.convertTo(im0, CV_8UC1, 255.0);
}
if (im1.type() == CV_64FC1)
{
im1.convertTo(im1, CV_8UC1, 255.0);
}
// Canny(im0, im0, canny_threshold1, canny_threshold2); // you can change this
// Canny(im1, im1, canny_threshold1, canny_threshold2);
// Ensure both images are of CV_32FC1 type
im0.convertTo(im0, CV_32FC1, 1.0 / 255.0);
im1.convertTo(im1, CV_32FC1, 1.0 / 255.0);
Mat F0[2], F1[2];
Mat f0, f1;
ForwardFFT(im0, F0);
ForwardFFT(im1, F1);
magnitude(F0[0], F0[1], f0);
magnitude(F1[0], F1[1], f1);
// Create filter
Mat h;
highpass(f0.size(), h);
// Apply it in freq domain
f0 = f0.mul(h);
f1 = f1.mul(h);
float log_base;
Mat f0lp, f1lp;
log_base = logpolar(f0, f0lp);
log_base = logpolar(f1, f1lp);
// Find rotation and scale
Point2d rotation_and_scale = cv::phaseCorrelate(f1lp, f0lp);
float angle = 180.0 * rotation_and_scale.y / f0lp.rows;
float scale = pow(log_base, rotation_and_scale.x);
// --------------
if (scale > 1.8)
{
rotation_and_scale = cv::phaseCorrelate(f1lp, f0lp);
angle = -180.0 * rotation_and_scale.y / f0lp.rows;
scale = 1.0 / pow(log_base, rotation_and_scale.x);
if (scale > 1.8)
{
cout << "Images are not compatible. Scale change > 1.8" << endl;
return RotatedRect();
}
}
// --------------
if (angle < -90.0)
{
angle += 180.0;
}
else if (angle > 90.0)
{
angle -= 180.0;
}
// Now rotate and scale fragment back, then find translation
Mat rot_mat = getRotationMatrix2D(Point(im1.cols / 2, im1.rows / 2), angle, 1.0 / scale);
// rotate and scale
Mat im1_rs;
warpAffine(im1, im1_rs, rot_mat, im1.size());
// find translation
Point2d tr = cv::phaseCorrelate(im1_rs, im0);
// compute rotated rectangle parameters
RotatedRect rr;
rr.center = tr + Point2d(im0.cols / 2, im0.rows / 2);
rr.angle = -angle;
rr.size.width = im1.cols / scale;
rr.size.height = im1.rows / scale;
//im0 = im0_tmp.clone();
//im1 = im1_tmp.clone();
return rr;
}
RotatedRect FFTMatch(const Mat& im0, const Mat& im1)
{
Mat im0_tmp = im0.clone();
Mat im1_tmp = im1.clone();
return LogPolarFFTTemplateMatch(im0_tmp, im1_tmp);
}
================================================
FILE: LidarIris/fftm/fftm.hpp
================================================
#ifndef _OPENCV_fftm_HPP_
#define _OPENCV_fftm_HPP_
#ifdef __cplusplus
#include "opencv2/core.hpp"
#include "opencv2/opencv.hpp"
//-----------------------------------------------------------------------------------------------------
// As input we need equal sized images, with the same aspect ratio,
// scale difference should not exceed 1.8 times.
//-----------------------------------------------------------------------------------------------------
cv::RotatedRect FFTMatch(const cv::Mat& im0, const cv::Mat& im1);
cv::RotatedRect LogPolarFFTTemplateMatch(cv::Mat& im0, cv::Mat& im1/*, double canny_threshold1=200, double canny_threshold2=100*/);
#endif
#endif
// 2019.09.17:
// remove canny
================================================
FILE: README.md
================================================
# LiDAR Iris for Loop-Closure Detection
**This repository accompanies our IROS 2020 paper [_LiDAR Iris for Loop-Closure Detection_](./iros2020.pdf).** In this paper, a global descriptor for a LiDAR point cloud, called LiDAR Iris, is proposed for fast and accurate loop-closure detection. A binary signature image can be obtained for each point cloud after several LoG-Gabor filtering and thresholding operations on the LiDAR-Iris image representation. Given two point clouds, their similarities can be calculated as the Hamming distance of two corresponding binary signature images extracted from the two point clouds, respectively. Our LiDAR-Iris method can achieve a pose-invariant loop-closure detection at a descriptor level with the Fourier transform of the LiDAR-Iris representation if assuming a 3D (x,y,yaw) pose space, although our method can generally be applied to a 6D pose space by re-aligning point clouds with an additional IMU sensor. Experimental results on five road-scene sequences demonstrate its excellent performance in loop-closure detection.
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## Publication
Ying Wang, [Zezhou Sun](https://github.com/SunZezhou), Cheng-Zhong Xu, Sanjay Sarma, Jian Yang, and [Hui Kong](https://sites.google.com/view/huikonglab/home), **LiDAR Iris for Loop-Closure Detection**, _IEEE International Conference on Intelligent Robotics and Systems (IROS) 2020 (**oral presentation**)_. [[PDF]](./iros2020.pdf) [[CODE]](https://github.com/BigMoWangying/LiDAR-Iris)
## Usage
#### 1. Requirement
```
1. cmake
2. PCL
3. OpenCV
```
#### 2. Build
```
cd ${YOUR_PROJECT_DIR}
mdkir build
cd build
cmake ..
make
```
#### 3. Run
```
./demo ../data/64line_1.pcd ../data/64line_2.pcd
```
================================================
FILE: data/64line.pcd
================================================
# .PCD v0.7 - Point Cloud Data file format
VERSION 0.7
FIELDS x y z intensity
SIZE 4 4 4 4
TYPE F F F F
COUNT 1 1 1 1
WIDTH 120776
HEIGHT 1
VIEWPOINT 0 0 0 1 0 0 0
POINTS 120776
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gitextract_wz8f4grx/ ├── CMakeLists.txt ├── LidarIris/ │ ├── LidarIris.cpp │ ├── LidarIris.h │ └── fftm/ │ ├── fftm.cpp │ └── fftm.hpp ├── README.md ├── data/ │ ├── 16line.pcd │ ├── 16line_1.pcd │ ├── 16line_2.pcd │ ├── 64line.pcd │ ├── 64line_1.pcd │ └── 64line_2.pcd └── demo.cpp
SYMBOL INDEX (10 symbols across 3 files) FILE: LidarIris/LidarIris.h function class (line 10) | class LidarIris FILE: LidarIris/fftm/fftm.cpp function Recomb (line 10) | void Recomb(Mat &src, Mat &dst) function ForwardFFT (line 25) | void ForwardFFT(Mat &Src, Mat *FImg, bool do_recomb = true) function InverseFFT (line 51) | void InverseFFT(Mat *FImg, Mat &Dst, bool do_recomb = true) function highpass (line 67) | void highpass(Size sz, Mat& dst) function logpolar (line 95) | float logpolar(Mat& src, Mat& dst) function RotatedRect (line 126) | RotatedRect LogPolarFFTTemplateMatch(Mat& im0, Mat& im1/*, double canny_... function RotatedRect (line 260) | RotatedRect FFTMatch(const Mat& im0, const Mat& im1) FILE: demo.cpp function OneCoupleCompare (line 10) | void OneCoupleCompare(string cloudFileName1, string cloudFileName2) function main (line 58) | int main(int argc, char *argv[])
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[
{
"path": "CMakeLists.txt",
"chars": 658,
"preview": "cmake_minimum_required (VERSION 3.8)\r\n\r\nSET(CMAKE_BUILD_TYPE Release)\r\n\r\nset(CMAKE_PREFIX_PATH ${CMAKE_PREFIX_PATH} \"/u"
},
{
"path": "LidarIris/LidarIris.cpp",
"chars": 8606,
"preview": "#include \"LidarIris.h\"\n#include \"fftm/fftm.hpp\"\n\ncv::Mat1b LidarIris::GetIris(const pcl::PointCloud<pcl::PointXYZ> &clou"
},
{
"path": "LidarIris/LidarIris.h",
"chars": 3173,
"preview": "#ifndef _LIDAR_IRIS_H_\n#define _LIDAR_IRIS_H_\n\n#include <vector>\n#include <flann/flann.hpp>\n#include <opencv2/opencv.hpp"
},
{
"path": "LidarIris/fftm/fftm.cpp",
"chars": 7897,
"preview": "#include \"opencv2/core.hpp\"\n#include \"opencv2/opencv.hpp\"\n\nusing namespace std;\nusing namespace cv;\n\n//-----------------"
},
{
"path": "LidarIris/fftm/fftm.hpp",
"chars": 698,
"preview": "#ifndef _OPENCV_fftm_HPP_\n#define _OPENCV_fftm_HPP_\n#ifdef __cplusplus\n\n#include \"opencv2/core.hpp\"\n#include \"opencv2/op"
},
{
"path": "README.md",
"chars": 1770,
"preview": "# LiDAR Iris for Loop-Closure Detection\n\n**This repository accompanies our IROS 2020 paper [_LiDAR Iris for Loop-Closure"
},
{
"path": "data/64line.pcd",
"chars": 4097900,
"preview": "# .PCD v0.7 - Point Cloud Data file format\nVERSION 0.7\nFIELDS x y z intensity\nSIZE 4 4 4 4\nTYPE F F F F\nCOUNT 1 1 1 1\nWI"
},
{
"path": "data/64line_1.pcd",
"chars": 4400842,
"preview": "# .PCD v0.7 - Point Cloud Data file format\nVERSION 0.7\nFIELDS x y z intensity\nSIZE 4 4 4 4\nTYPE F F F F\nCOUNT 1 1 1 1\nWI"
},
{
"path": "data/64line_2.pcd",
"chars": 4426827,
"preview": "# .PCD v0.7 - Point Cloud Data file format\nVERSION 0.7\nFIELDS x y z intensity\nSIZE 4 4 4 4\nTYPE F F F F\nCOUNT 1 1 1 1\nWI"
},
{
"path": "demo.cpp",
"chars": 1929,
"preview": "#include <iostream>\n#include <vector>\n#include <string>\n#include <ctime>\n#include <pcl/io/pcd_io.h>\n#include \"LidarIris."
}
]
// ... and 3 more files (download for full content)
About this extraction
This page contains the full source code of the JoestarK/LiDAR-Iris GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 13 files (12.4 MB), approximately 3.2M tokens, and a symbol index with 10 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.