├── C++ └── .gitkeep ├── README.md ├── homework_PPT_Zou_2018 ├── .DS_Store ├── PPT │ ├── vlp_lecture_01.pdf │ ├── vlp_lecture_02.pdf │ ├── vlp_lecture_03.pdf │ ├── vlp_lecture_04.pdf │ ├── vlp_lecture_05.pdf │ ├── vlp_lecture_06.pdf │ ├── vlp_lecture_07.pdf │ ├── vlp_lecture_08.pdf │ ├── vlp_lecture_09.pdf │ ├── vlp_lecture_10.pdf │ ├── vlp_lecture_11.pdf │ ├── vlp_lecture_12.pdf │ ├── vlp_lecture_13.pdf │ ├── vlp_lecture_14.pdf │ └── vlp_lecture_15.pdf ├── Partial_code │ ├── HarrisDetector │ │ ├── CMakeLists.txt │ │ ├── main.cpp │ │ └── test1.bmp │ └── Histogram_specialization │ │ ├── CMakeLists.txt │ │ ├── main.cpp │ │ ├── test1.bmp │ │ ├── test2.bmp │ │ └── test3.bmp └── homework │ ├── .DS_Store │ ├── 作业5-数据.zip │ ├── 作业5-数据 │ ├── icp_x.txt │ ├── icp_xy.mat │ └── icp_y.txt │ ├── 作业五.docx │ ├── 作业六.docx │ ├── 作业四.docx │ └── 期末大作业.docx └── vslam_fourteen_lectures ├── .gitkeep ├── BApnp ├── CMakeLists.txt ├── GN-BA.cpp ├── p2d.txt ├── p3d.txt └── result-ba.png ├── EigenDense ├── .DS_Store ├── CMakeLists.txt └── main.cpp ├── EigenGeometry ├── .DS_Store ├── CMakeLists.txt └── main.cpp ├── computeORB ├── 1.png ├── 2.png ├── CMakeLists.txt ├── computeORB.cpp ├── feat1.png └── matches.png ├── computerRt ├── CMakeLists.txt ├── E2Rt.cpp └── result.png ├── direct_method ├── 000001.png ├── 000002.png ├── 000003.png ├── 000004.png ├── 000005.png ├── 1_c.png ├── 1_r.png ├── 2_c.png ├── 2_r.png ├── 3_c.png ├── 3_r.png ├── 4_c.png ├── 4_r.png ├── CMakeLists.txt ├── direct_method.cpp ├── disparity.png ├── left.png ├── multi_layer_result.png ├── right.png └── sigle_layer_result.png ├── disparity ├── CMakeLists.txt ├── disparity.cpp ├── disparity.png ├── left.png ├── result1.png └── right.png ├── gauessnewton ├── CMakeLists.txt ├── gaussnewton.cpp └── result.png ├── icp ├── CMakeLists.txt ├── ba_result.png ├── ba_result1.png ├── cmake_modules │ ├── FindCSparse.cmake │ └── FindG2O.cmake ├── compare.txt ├── icp.cpp ├── svd_result.png ├── svd_result1.png └── 原始图像.png ├── optical_flow ├── 1.png ├── 2.png ├── CMakeLists.txt ├── multi_f.png ├── multi_i.png ├── opencv_op.png ├── optical_flow.cpp ├── single_f.png └── single_i.png ├── trajectError ├── .DS_Store ├── CMakeLists.txt ├── estimated.txt ├── groundtruth.txt └── tracjecError.cpp ├── trajectory ├── .DS_Store ├── CMakeLists.txt ├── draw_trajectory.cpp └── trajectory.txt └── undistort ├── CMakeLists.txt ├── build ├── CMakeCache.txt ├── CMakeFiles │ ├── 3.12.3 │ │ ├── CMakeCCompiler.cmake │ │ ├── CMakeCXXCompiler.cmake │ │ ├── CMakeDetermineCompilerABI_C.bin │ │ ├── CMakeDetermineCompilerABI_CXX.bin │ │ ├── CMakeSystem.cmake │ │ ├── CompilerIdC │ │ │ ├── CMakeCCompilerId.c │ │ │ └── a.out │ │ └── CompilerIdCXX │ │ │ ├── CMakeCXXCompilerId.cpp │ │ │ └── a.out │ ├── CMakeDirectoryInformation.cmake │ ├── CMakeOutput.log │ ├── Makefile.cmake │ ├── Makefile2 │ ├── TargetDirectories.txt │ ├── cmake.check_cache │ ├── feature_tests.bin │ ├── feature_tests.c │ ├── feature_tests.cxx │ ├── progress.marks │ └── undistort.dir │ │ ├── CXX.includecache │ │ ├── DependInfo.cmake │ │ ├── build.make │ │ ├── cmake_clean.cmake │ │ ├── depend.internal │ │ ├── depend.make │ │ ├── flags.make │ │ ├── link.txt │ │ ├── progress.make │ │ └── undistort_image.cpp.o ├── Makefile ├── cmake_install.cmake └── undistort ├── result.png ├── test.png └── undistort_image.cpp /C++/.gitkeep: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/XLMaverick/Visual-Localization-Percessing/34549bcd1ec11d87b8466d3c264706a325ffdd11/C++/.gitkeep -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Visual-Localization-Percessing 2 | 该仓库主要包含了三大部分： 3 | 4 | - 《视觉SLAM十四讲》相关习题的解答和代码； 5 | - 邹丹平老师视觉定位的课件； 6 | - C++相关的操作； 7 | 8 | ### 关于《视觉SLAM十四讲》部分： 9 | 以高翔上课的视频为主线，完成相应的课后题为主要任务。这一部分可以分为文档部分和代码部分。文档部分可以参考我的个人主页https://www.xlmaverick.me 10 | 本仓库中主要是相应的习题配套代码。 11 | 代码部分按章节存放，包含相应的cpp文件和CmakeList文件。需要安装的依赖包没有具体的说明，可以参考CmakeList文件自行安装。 12 | 13 | ### 邹丹平老师视觉定位的课件部分： 14 | 这是邹老师相应的教学课件，偏重于SFM部分，相当于《视觉SLAM十四讲》中的前十讲，没有包含回环检测和建图的部分，在课程的结束部分对SLAM进行了现状和未来的探讨。同样，也有相应的习题作业，但是由于和《十四讲》有重复的地方，上课过程中也只完成了相应的代码，尚未整理和文档编写，所以暂时未完整添加进来。 15 | 16 | ### C++相关的操作 17 | 这一部分主要包含两个部分，数据结构算法部分和C++知识部分，这一个主要是平时整理代码和回顾总结。 18 | -------------------------------------------------------------------------------- /homework_PPT_Zou_2018/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/XLMaverick/Visual-Localization-Percessing/34549bcd1ec11d87b8466d3c264706a325ffdd11/homework_PPT_Zou_2018/.DS_Store -------------------------------------------------------------------------------- /homework_PPT_Zou_2018/PPT/vlp_lecture_01.pdf: -------------------------------------------------------------------------------- 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double getSobelEdge(const Mat& imgSrc, Mat& imgDst, double thresh = -1, int direction = SOBEL_BOTH); 26 | static double getCannyThresh(const Mat& inputArray, double percentage); 27 | void getCannyEdge(const Mat& imgSrc, Mat& imgDst, double lowThresh = -1, double highThresh = -1, double sigma = 1); 28 | 29 | 30 | void detectHarrisCorners(const Mat& imgSrc, Mat& imgDst, double alpha); 31 | void drawCornerOnImage(Mat& image, const Mat&binary); 32 | void detectHarrisLaplace(const Mat& imgSrc, Mat& imgDst); 33 | } 34 | 35 | void feat::detectHarrisCorners(const Mat& imgSrc, Mat& imgDst, double alpha) 36 | { 37 | Mat gray; 38 | if (imgSrc.channels() == 3) 39 | { 40 | cvtColor(imgSrc, gray, CV_BGR2GRAY); 41 | } 42 | else 43 | { 44 | gray = imgSrc.clone(); 45 | } 46 | gray.convertTo(gray, CV_64F); 47 | 48 | Mat xKernel = (Mat_(1,3) << -1, 0, 1); 49 | Mat yKernel = xKernel.t(); 50 | 51 | Mat Ix,Iy; 52 | filter2D(gray, Ix, CV_64F, xKernel); 53 | filter2D(gray, Iy, CV_64F, yKernel); 54 | 55 | Mat Ix2,Iy2,Ixy; 56 | Ix2 = Ix.mul(Ix); 57 | Iy2 = Iy.mul(Iy); 58 | Ixy = Ix.mul(Iy); 59 | 60 | Mat gaussKernel = getGaussianKernel(7, 1); 61 | filter2D(Ix2, Ix2, CV_64F, gaussKernel); 62 | filter2D(Iy2, Iy2, CV_64F, gaussKernel); 63 | filter2D(Ixy, Ixy, CV_64F, gaussKernel); 64 | 65 | 66 | Mat cornerStrength(gray.size(), gray.type()); 67 | for (int i = 0; i < gray.rows; i++) 68 | { 69 | for (int j = 0; j < gray.cols; j++) 70 | { 71 | double det_m = Ix2.at(i,j) * Iy2.at(i,j) - Ixy.at(i,j) * Ixy.at(i,j); 72 | double trace_m = Ix2.at(i,j) + Iy2.at(i,j); 73 | cornerStrength.at(i,j) = det_m - alpha * trace_m *trace_m; 74 | } 75 | } 76 | // threshold 77 | double maxStrength; 78 | minMaxLoc(cornerStrength, NULL, &maxStrength, NULL, NULL); 79 | Mat dilated; 80 | Mat localMax; 81 | dilate(cornerStrength, dilated, Mat()); 82 | compare(cornerStrength, dilated, localMax, CMP_EQ); 83 | 84 | 85 | Mat cornerMap; 86 | double qualityLevel = 0.01; 87 | double thresh = qualityLevel * maxStrength; 88 | cornerMap = cornerStrength > thresh; 89 | bitwise_and(cornerMap, localMax, cornerMap); 90 | 91 | imgDst = cornerMap.clone(); 92 | 93 | } 94 | 95 | void feat::drawCornerOnImage(Mat& image, const Mat& binary) 96 | { 97 | Mat_::const_iterator it = binary.begin(); 98 | Mat_::const_iterator itd = binary.end(); 99 | for (int i = 0; it != itd; it++, i++) 100 | { 101 | if (*it) 102 | circle(image, Point(i%image.cols, i / image.cols), 3, Scalar(0, 255, 0), 1); 103 | } 104 | } 105 | 106 | int main(int argc, char** argv ) 107 | { 108 | Mat image; 109 | Mat cornerMap; 110 | image = imread("../test1.bmp",IMREAD_GRAYSCALE ); 111 | if ( !image.data ) 112 | { 113 | printf("No image data \n"); 114 | printf("Please check the path and name of image_origianl \n"); 115 | return -1; 116 | } 117 | 118 | feat::detectHarrisCorners(image, cornerMap,0.001); 119 | feat::drawCornerOnImage(image, cornerMap); 120 | 121 | namedWindow("Display Image", WINDOW_AUTOSIZE ); 122 | imshow("Display Image", cornerMap); 123 | waitKey(0); 124 | 125 | return 0; 126 | } 127 | -------------------------------------------------------------------------------- /homework_PPT_Zou_2018/Partial_code/HarrisDetector/test1.bmp: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/XLMaverick/Visual-Localization-Percessing/34549bcd1ec11d87b8466d3c264706a325ffdd11/homework_PPT_Zou_2018/Partial_code/HarrisDetector/test1.bmp -------------------------------------------------------------------------------- /homework_PPT_Zou_2018/Partial_code/Histogram_specialization/CMakeLists.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/XLMaverick/Visual-Localization-Percessing/34549bcd1ec11d87b8466d3c264706a325ffdd11/homework_PPT_Zou_2018/Partial_code/Histogram_specialization/CMakeLists.txt -------------------------------------------------------------------------------- /homework_PPT_Zou_2018/Partial_code/Histogram_specialization/main.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | using namespace cv; 4 | 5 | cv::Mat histogram_specilalization(cv::Mat srcImage, cv::Mat dstImage) 6 | { 7 | Mat output = srcImage.clone(); 8 | 9 | int grayArray[256]; 10 | float srcpdf[256] = {0.0f}; 11 | float dstpdf[256] = {0.0f}; 12 | int gray_tag = 0; 13 | float gray_sum = 0.0f; 14 | 15 | memset(grayArray,0,sizeof(grayArray)); 16 | for(size_t nrow = 0; nrow < srcImage.rows; nrow++) 17 | { 18 | for(size_t ncol = 0;ncol < srcImage.cols; ncol++) 19 | { 20 | gray_tag = srcImage.at(nrow,ncol); 21 | grayArray[gray_tag]++; 22 | } 23 | } 24 | gray_sum = 0.0f; 25 | for(int i = 0;i < 256;i++) 26 | { 27 | gray_sum = gray_sum+grayArray[i]; 28 | srcpdf[i] = gray_sum / (srcImage.rows*srcImage.cols); 29 | // std::cout<(nrow, ncol); 38 | grayArray[gray_tag]++; 39 | } 40 | } 41 | gray_sum = 0.0f; 42 | for(int i=0;i < 256;i++) 43 | { 44 | gray_sum = gray_sum + grayArray[i]; 45 | dstpdf[i] = gray_sum / (dstImage.rows * dstImage.cols); 46 | //std::cout< abs(srcpdf[i]-dstpdf[j])) 57 | { 58 | minMap = abs(srcpdf[i]-dstpdf[j]); 59 | minTag = j; 60 | } 61 | } 62 | histogram_history[i] = minTag; 63 | } 64 | 65 | for(size_t nrow = 0;nrow(nrow,ncol); 70 | output.at(nrow,ncol) = histogram_history[temp]; 71 | } 72 | } 73 | 74 | return output; 75 | } 76 | 77 | cv::Mat histogram_equalization(cv::Mat srcImage) 78 | { 79 | 80 | Mat output = srcImage.clone(); 81 | 82 | int grayArray[256]; 83 | float srcpdf[256] = {0.0f}; 84 | 85 | int gray_tag = 0; 86 | float gray_sum = 0.0f; 87 | 88 | memset(grayArray,0,sizeof(grayArray)); 89 | for(size_t nrow = 0; nrow < srcImage.rows; nrow++) 90 | { 91 | for(size_t ncol = 0;ncol < srcImage.cols; ncol++) 92 | { 93 | gray_tag = srcImage.at(nrow,ncol); 94 | grayArray[gray_tag]++; 95 | } 96 | } 97 | gray_sum = 0.0f; 98 | for(int i = 0;i < 256;i++) 99 | { 100 | gray_sum = gray_sum+grayArray[i]; 101 | srcpdf[i] = gray_sum / (srcImage.rows*srcImage.cols); 102 | // std::cout<(nrow,ncol); 116 | output.at(nrow,ncol) = histogram_history[temp]; 117 | } 118 | } 119 | 120 | return output; 121 | 122 | } 123 | 124 | void histogram_show(cv::Mat srcImage) 125 | { 126 | 127 | int channels = 0; 128 | 129 | MatND dstHist; 130 | 131 | int histSize[] = { 256 }; 132 | 133 | 134 | float midRanges[] = { 0, 256 }; 135 | const float *ranges[] = { midRanges }; 136 | 137 | calcHist(&srcImage, 1, &channels, Mat(), dstHist, 1, histSize, ranges, true, false); 138 | 139 | Mat drawImage = Mat::zeros(Size(256, 256), CV_8UC3); 140 | double g_dHistMaxValue; 141 | minMaxLoc(dstHist, 0, &g_dHistMaxValue, 0, 0); 142 | for (int i = 0; i < 256; i++) 143 | { 144 | int value = cvRound(dstHist.at(i) * 256 * 0.9 / g_dHistMaxValue); 145 | 146 | line(drawImage, Point(i, drawImage.rows - 1), Point(i, drawImage.rows - 1 - value), Scalar(255, 255, 255)); 147 | } 148 | 149 | imshow("【图片直方图】", drawImage); 150 | } 151 | 152 | 153 | int main(int argc, char** argv ) 154 | { 155 | Mat image_origianl, image_target, image_adjust; 156 | image_origianl = imread("../test2.bmp",IMREAD_GRAYSCALE ); 157 | if ( !image_origianl.data ) 158 | { 159 | printf("No image data \n"); 160 | printf("Please check the path and name of image_origianl \n"); 161 | return -1; 162 | } 163 | image_target = imread("../test3.bmp",IMREAD_GRAYSCALE ); 164 | if ( !image_target.data ) 165 | { 166 | printf("No image data \n"); 167 | printf("Please check the path and name of image_target \n"); 168 | return -1; 169 | } 170 | 171 | histogram_show(image_origianl); 172 | image_adjust = histogram_equalization(image_origianl); 173 | histogram_show(image_adjust); 174 | 175 | namedWindow("Display Image", WINDOW_AUTOSIZE ); 176 | imshow("Display Image", image_adjust); 177 | waitKey(0); 178 | 179 | return 0; 180 | } 181 | -------------------------------------------------------------------------------- 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/homework_PPT_Zou_2018/homework/作业5-数据/icp_x.txt: -------------------------------------------------------------------------------- 1 | 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-------------------------------------------------------------------------------- https://raw.githubusercontent.com/XLMaverick/Visual-Localization-Percessing/34549bcd1ec11d87b8466d3c264706a325ffdd11/vslam_fourteen_lectures/.gitkeep -------------------------------------------------------------------------------- /vslam_fourteen_lectures/BApnp/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | CMAKE_MINIMUM_REQUIRED(VERSION 2.8) 2 | 3 | SET(BUILD_TYPE Release) 4 | 5 | PROJECT(pngBA) 6 | 7 | SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 -O3") 8 | 9 | set(Sophus_DIR /home/yxl/workspace/gaoxiangSlambook/slambook/3rdparty/Sophus/build/) 10 | FIND_PACKAGE(Sophus REQUIRED) 11 | 12 | INCLUDE_DIRECTORIES(${Sophus_INCLUDE_DIRS} 13 | "/usr/include/eigen3") 14 | 15 | MESSAGE(STATUS "Sophus INCLUD: ${Sophus_INCLUDE_DIRS} \n ${Sophus_LIBRARIES}") 16 | 17 | 18 | ADD_EXECUTABLE(${PROJECT_NAME} GN-BA.cpp) 19 | 20 | TARGET_LINK_LIBRARIES(${PROJECT_NAME} 21 | ${Sophus_LIBRARIES}) -------------------------------------------------------------------------------- /vslam_fourteen_lectures/BApnp/GN-BA.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | 4 | using namespace Eigen; 5 | 6 | #include 7 | #include 8 | #include 9 | #include 10 | 11 | #include "sophus/se3.h" 12 | 13 | using namespace std; 14 | 15 | typedef vector> VecVector3d; 16 | typedef vector> VecVector2d; 17 | typedef Matrix Vector6d; 18 | 19 | string p3d_file = "../p3d.txt"; 20 | string p2d_file = "../p2d.txt"; 21 | 22 | int main(int argc, char **argv) { 23 | 24 | VecVector2d p2d; 25 | VecVector3d p3d; 26 | Matrix3d K; 27 | double fx = 520.9, fy = 521.0, cx = 325.1, cy = 249.7; 28 | K << fx, 0, cx, 0, fy, cy, 0, 0, 1; 29 | 30 | ifstream p3dfile; 31 | p3dfile.open(p3d_file); 32 | if(!p3dfile) 33 | { 34 | cout<<"open p3d.txt error!"<>p3dtemp[0]>>p3dtemp[1]>>p3dtemp[2]; 42 | p3d.push_back(p3dtemp); 43 | 44 | } 45 | 46 | ifstream p2dfile; 47 | p2dfile.open(p2d_file); 48 | if(!p2dfile) 49 | { 50 | cout<<"open p2d.txt error!"<>p2dtemp[0]>>p2dtemp[1]; 58 | p2d.push_back(p2dtemp); 59 | 60 | } 61 | 62 | assert(p3d.size() == p2d.size()); 63 | 64 | int iterations = 100; 65 | double cost = 0, lastCost = 0; 66 | int nPoints = p3d.size(); 67 | cout << "points: " << nPoints << endl; 68 | 69 | Sophus::SE3 T_esti(Matrix3d::Identity(),Vector3d::Zero()); // estimated pose 70 | 71 | for (int iter = 0; iter < iterations; iter++) 72 | { 73 | Matrix H = Matrix::Zero(); 74 | Vector6d b = Vector6d::Zero(); 75 | Vector2d e; 76 | cost = 0; 77 | for (int i = 0; i < nPoints; i++) 78 | { 79 | Vector3d pc = T_esti*p3d[i]; 80 | Vector3d e_temp = Vector3d(p2d[i][0],p2d[i][1],1) - K*pc/pc[2]; 81 | e[0] = e_temp[0]; 82 | e[1] = e_temp[1]; 83 | cost += 0.5*e.transpose()*e; 84 | double x = pc[0], y = pc[1], z = pc[2]; 85 | Matrix J; 86 | J(0,0) = -fx/z; 87 | J(0,2) = fx*x/(z*z); 88 | J(0,3) = fx*x*y/(z*z); 89 | J(0,4) = -fx - fx*(x*x)/(z*z); 90 | J(0,5) = fx*y/z; 91 | J(1,1) = -fy/z; 92 | J(1,2) = fy*y/(z*z); 93 | J(1,3) = fy+fy*(y*y)/(z*z); 94 | J(1,4) = -fy*x*y/(z*z); 95 | J(1,5) = -fy*x/z; 96 | 97 | H +=J.transpose()*J; 98 | b +=-J.transpose()*e; 99 | } 100 | Vector6d dx; 101 | dx = H.ldlt().solve(b); 102 | 103 | if (isnan(dx[0])) 104 | { 105 | cout << "result is nan!" << endl; 106 | break; 107 | } 108 | 109 | if (iter > 0 && cost >= lastCost) 110 | { 111 | // cost increase, update is not good 112 | cout << "cost: " << cost << ", last cost: " << lastCost << endl; 113 | break; 114 | } 115 | 116 | T_esti = Sophus::SE3::exp(dx)*T_esti; 117 | 118 | lastCost = cost; 119 | 120 | cout << "iteration " << iter << " cost=" << cout.precision(12) << cost << endl; 121 | } 122 | 123 | cout << "estimated pose: \n" << T_esti.matrix() << endl; 124 | return 0; 125 | } 126 | -------------------------------------------------------------------------------- /vslam_fourteen_lectures/BApnp/p2d.txt: -------------------------------------------------------------------------------- 1 | 323 109 2 | 231 219 3 | 65 308 4 | 185 292 5 | 61 287 6 | 414 188 7 | 37 264 8 | 510 266 9 | 528 272 10 | 279 236 11 | 135 182 12 | 350 232 13 | 500 263 14 | 278.4 236.4 15 | 422 188 16 | 414 188 17 | 62.4 289.2 18 | 231.6 219.6 19 | 50.4 273.6 20 | 528 272.4 21 | 337.2 114 22 | 324 111.6 23 | 502.8 262.8 24 | 350.4 231.6 25 | 185 292 26 | 499.2 262.8 27 | 231.552 219.456 28 | 349.92 230.4 29 | 478.08 282.24 30 | 420.48 187.2 31 | 413.28 187.2 32 | 501.12 267.84 33 | 277.92 236.16 34 | 184.32 290.88 35 | 514.944 271.296 36 | 349.056 229.824 37 | 231.552 219.456 38 | 501.12 269.568 39 | 321.408 105.408 40 | 419.904 188.352 41 | 413.28 187.2 42 | 324.864 110.592 43 | 205.632 267.84 44 | 55.296 273.024 45 | 502.848 262.656 46 | 46.08 273.6 47 | 184.32 290.88 48 | 136.858 236.39 49 | 418.867 186.624 50 | 350.438 230.17 51 | 337.997 111.974 52 | 232.243 217.728 53 | 184.896 290.304 54 | 151.373 240.538 55 | 136.858 236.39 56 | 232.243 217.728 57 | 350.438 230.17 58 | 144.323 233.902 59 | 281.18 236.39 60 | 186.624 290.304 61 | 349.36 367.276 62 | 343.388 364.29 63 | 346.374 226.935 64 | 232.907 217.977 65 | 217.977 194.089 66 | 280.683 235.893 67 | 354.735 365.485 68 | 343.388 364.29 69 | 232.907 214.991 70 | 347.569 225.74 71 | 283.071 236.49 72 | 150.494 243.656 73 | 347.569 336.819 74 | 272.322 250.823 75 | 186.325 290.238 76 | 333.236 240.073 -------------------------------------------------------------------------------- /vslam_fourteen_lectures/BApnp/p3d.txt: -------------------------------------------------------------------------------- 1 | -0.0374123 -0.830816 2.7448 2 | -0.243698 -0.117719 1.5848 3 | -0.627753 0.160186 1.3396 4 | -0.323443 0.104873 1.4266 5 | -0.627221 0.101454 1.3116 6 | 0.402045 -0.341821 2.2068 7 | -0.687785 0.0430873 1.2976 8 | 0.627166 -0.0166317 1.5202 9 | 0.65817 -0.00198633 1.4784 10 | -0.0897164 -0.073607 1.5526 11 | -0.522843 -0.214436 1.4956 12 | 0.139792 -0.0954137 1.5202 13 | 0.594266 -0.0256024 1.5332 14 | -0.0891202 -0.0718189 1.5526 15 | 0.427399 -0.333001 2.1552 16 | 0.399677 -0.344018 2.1938 17 | -0.627209 0.10885 1.3158 18 | -0.244307 -0.120761 1.5848 19 | -0.637861 0.0641397 1.2706 20 | 0.661009 -0.000283745 1.4784 21 | 0.032143 -0.816064 2.7448 22 | -0.0374123 -0.822386 2.7448 23 | 0.601751 -0.0249094 1.5268 24 | 0.136873 -0.0948301 1.5202 25 | -0.329006 0.106137 1.4438 26 | 0.593677 -0.0250138 1.5332 27 | -0.24332 -0.123909 1.5784 28 | 0.136061 -0.0987584 1.5268 29 | 0.515821 0.0343076 1.4438 30 | 0.42442 -0.335979 2.1552 31 | 0.401698 -0.341997 2.1938 32 | 0.590593 -0.00582347 1.502 33 | -0.0891202 -0.0746797 1.5526 34 | -0.331572 0.106534 1.4492 35 | 0.624455 -0.00247196 1.4838 36 | 0.136061 -0.0987584 1.5268 37 | -0.242554 -0.118279 1.5848 38 | 0.587601 -0.00248262 1.4902 39 | -0.0467705 -0.851215 2.7448 40 | 0.423345 -0.33438 2.168 41 | 0.403968 -0.3497 2.2196 42 | -0.0283474 -0.81778 2.7244 43 | -0.313646 0.0344556 1.5988 44 | -0.637121 0.0619232 1.2878 45 | 0.607097 -0.0227993 1.5268 46 | -0.649687 0.0695246 1.2706 47 | -0.336318 0.104556 1.4664 48 | -0.611718 -0.0528933 1.7914 49 | 0.416153 -0.340132 2.168 50 | 0.135048 -0.0997712 1.5268 51 | 0.0351785 -0.823904 2.7448 52 | -0.238348 -0.122484 1.5848 53 | -0.327565 0.101407 1.4492 54 | -0.474742 -0.0386718 1.5138 55 | -0.616088 -0.05458 1.8 56 | -0.23961 -0.123745 1.5848 57 | 0.133832 -0.104632 1.5268 58 | -0.593113 -0.0721167 1.8086 59 | -0.0864075 -0.0767394 1.5526 60 | -0.331102 0.101785 1.4546 61 | 0.147499 0.242462 1.163 62 | 0.129166 0.232126 1.1782 63 | 0.132374 -0.101716 1.5268 64 | -0.235068 -0.123745 1.5848 65 | -0.397808 -0.253889 2.0486 66 | -0.0793163 -0.0681207 1.559 67 | 0.16172 0.233819 1.1598 68 | 0.139069 0.235996 1.1706 69 | -0.236885 -0.127379 1.5848 70 | 0.128316 -0.111731 1.5202 71 | -0.0825507 -0.0714003 1.5526 72 | -0.476188 -0.0383825 1.5138 73 | 0.144134 0.188769 1.2878 74 | -0.109385 -0.0301998 1.6344 75 | -0.326273 0.102792 1.4492 76 | 0.0766801 -0.0599636 1.5332 -------------------------------------------------------------------------------- /vslam_fourteen_lectures/BApnp/result-ba.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/XLMaverick/Visual-Localization-Percessing/34549bcd1ec11d87b8466d3c264706a325ffdd11/vslam_fourteen_lectures/BApnp/result-ba.png -------------------------------------------------------------------------------- /vslam_fourteen_lectures/EigenDense/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/XLMaverick/Visual-Localization-Percessing/34549bcd1ec11d87b8466d3c264706a325ffdd11/vslam_fourteen_lectures/EigenDense/.DS_Store -------------------------------------------------------------------------------- /vslam_fourteen_lectures/EigenDense/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | cmake_minimum_required(VERSION 2.8) 2 | project(homework_1th) 3 | 4 | set(CMAKE_BUILD_TYPE "Release") 5 | set(CMAKE_CXX_FLAGS "-std=c++11") 6 | 7 | include_directories("/usr/include/eigen3") 8 | add_executable(eigenMatrix main.cpp) -------------------------------------------------------------------------------- /vslam_fourteen_lectures/EigenDense/main.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | #include 4 | #include 5 | 6 | using namespace std; 7 | 8 | #define SIZE 100 9 | 10 | int main(int argc, char** argv) 11 | { 12 | /* Eigen最大的固定数组为50，当超过50时，如果没有使用动态初始化的方式，可以运行，但是时间效率非常的慢*/ 13 | // Eigen::Matrix matrixA; 14 | // Eigen::Matrix matrixb; 15 | 16 | //两种动态动态初始化的方式。 17 | // Eigen::MatrixXd matrixA; 18 | // Eigen::MatrixXd matrixb; 19 | 20 | Eigen::Matrix matrixA; 21 | Eigen::Matrix matrixb; 22 | 23 | matrixA = Eigen::MatrixXd::Random(SIZE,SIZE); 24 | matrixb = Eigen::MatrixXd::Random(SIZE,1); 25 | 26 | /*将系数矩阵变换为正定对称矩阵，如果没有这一步，Cholesky分解与前两个结果不一致 */ 27 | matrixA = matrixA.transpose()*matrixA; 28 | 29 | Eigen::Matrix x; 30 | clock_t time_sst = clock(); 31 | x = matrixA.inverse()*matrixb; 32 | cout< 2 | #include 3 | #include 4 | 5 | using namespace std; 6 | 7 | int main(int argc, char** argv) 8 | { 9 | 10 | /*四元数定义的顺序和打印的顺序不一致，需要注意*/ 11 | Eigen::Quaterniond q1(0.7,0.3,0.2,0.2); 12 | cout<<"quaterniond q1 "< 2 | #include 3 | #include 4 | #include 5 | #include 6 | 7 | using namespace std; 8 | using namespace Eigen; 9 | 10 | int main(int argc, char **argv) { 11 | 12 | // 给定Essential矩阵 13 | Matrix3d E; 14 | E << -0.0203618550523477, -0.4007110038118445, -0.03324074249824097, 15 | 0.3939270778216369, -0.03506401846698079, 0.5857110303721015, 16 | -0.006788487241438284, -0.5815434272915686, -0.01438258684486258; 17 | 18 | // 待计算的R,t 19 | Matrix3d R; 20 | Vector3d t; 21 | 22 | JacobiSVD svd(E,ComputeThinU | ComputeThinV); 23 | Matrix3d U = svd.matrixU(); 24 | Matrix3d V = svd.matrixV(); 25 | Matrix3d Sigma = U.inverse()*E*V.transpose().inverse(); 26 | // cout<<"U:\n"< tao = {Sigma(0,0),Sigma(1,1),Sigma(2,2)}; 28 | sort(tao.begin(), tao.end()); 29 | Matrix3d SigmaFix = Matrix3d::Zero(); 30 | double tao_mead = (tao[1]+tao[2])*0.5; 31 | SigmaFix(0,0) = tao_mead; 32 | SigmaFix(1,1) = tao_mead; 33 | cout<<"Sigma after fix: \n"< 2 | #include 3 | #include 4 | #include 5 | #include 6 | #include 7 | #include 8 | 9 | using namespace std; 10 | 11 | typedef vector> VecVector2d; 12 | 13 | // Camera intrinsics 14 | // 内参 15 | double fx = 718.856, fy = 718.856, cx = 607.1928, cy = 185.2157; 16 | // 基线 17 | double baseline = 0.573; 18 | // paths 19 | string left_file = "../left.png"; 20 | string disparity_file = "../disparity.png"; 21 | boost::format fmt_others("../%06d.png"); // other files 22 | 23 | // useful typedefs 24 | typedef Eigen::Matrix Matrix6d; 25 | typedef Eigen::Matrix Matrix26d; 26 | typedef Eigen::Matrix Vector6d; 27 | 28 | // TODO implement this function 29 | /** 30 | * pose estimation using direct method 31 | * @param img1 32 | * @param img2 33 | * @param px_ref 34 | * @param depth_ref 35 | * @param T21 36 | */ 37 | void DirectPoseEstimationMultiLayer( 38 | const cv::Mat &img1, 39 | const cv::Mat &img2, 40 | const VecVector2d &px_ref, 41 | const vector depth_ref, 42 | Sophus::SE3 &T21 43 | ); 44 | 45 | // TODO implement this function 46 | /** 47 | * pose estimation using direct method 48 | * @param img1 49 | * @param img2 50 | * @param px_ref 51 | * @param depth_ref 52 | * @param T21 53 | */ 54 | void DirectPoseEstimationSingleLayer( 55 | const cv::Mat &img1, 56 | const cv::Mat &img2, 57 | const VecVector2d &px_ref, 58 | const vector depth_ref, 59 | Sophus::SE3 &T21 60 | ); 61 | 62 | // bilinear interpolation 63 | inline float GetPixelValue(const cv::Mat &img, float x, float y) { 64 | uchar *data = &img.data[int(y) * img.step + int(x)]; 65 | float xx = x - floor(x); 66 | float yy = y - floor(y); 67 | return float( 68 | (1 - xx) * (1 - yy) * data[0] + 69 | xx * (1 - yy) * data[1] + 70 | (1 - xx) * yy * data[img.step] + 71 | xx * yy * data[img.step + 1] 72 | ); 73 | } 74 | 75 | int main(int argc, char **argv) { 76 | 77 | cv::Mat left_img = cv::imread(left_file, 0); 78 | cv::Mat disparity_img = cv::imread(disparity_file, 0); 79 | 80 | // let's randomly pick pixels in the first image and generate some 3d points in the first image's frame 81 | cv::RNG rng; 82 | int nPoints = 1000; 83 | int boarder = 40; 84 | VecVector2d pixels_ref; 85 | vector depth_ref; 86 | 87 | // generate pixels in ref and load depth data 88 | for (int i = 0; i < nPoints; i++) 89 | { 90 | int x = rng.uniform(boarder, left_img.cols - boarder); // don't pick pixels close to boarder 91 | int y = rng.uniform(boarder, left_img.rows - boarder); // don't pick pixels close to boarder 92 | int disparity = disparity_img.at(y, x); 93 | double depth = fx * baseline / disparity; // you know this is disparity to depth 94 | depth_ref.push_back(depth); 95 | pixels_ref.push_back(Eigen::Vector2d(x, y)); 96 | } 97 | 98 | // estimates 01~05.png's pose using this information 99 | Sophus::SE3 T_cur_ref;//(Eigen::Matrix3d::Identity(),Eigen::Vector3d::Zero()); 100 | 101 | for (int i = 1; i < 6; i++) 102 | { // 1~10 103 | cv::Mat img = cv::imread((fmt_others % i).str(), 0); 104 | // DirectPoseEstimationSingleLayer(left_img, img, pixels_ref, depth_ref, T_cur_ref); // first you need to test single layer 105 | DirectPoseEstimationMultiLayer(left_img, img, pixels_ref, depth_ref, T_cur_ref); 106 | cout << "T_cur_ref = \n" << T_cur_ref.matrix() << endl; 107 | } 108 | } 109 | 110 | void DirectPoseEstimationSingleLayer( 111 | const cv::Mat &img1, 112 | const cv::Mat &img2, 113 | const VecVector2d &px_ref, 114 | const vector depth_ref, 115 | Sophus::SE3 &T21 116 | ) { 117 | 118 | // parameters 119 | int half_patch_size = 4; 120 | int iterations = 100; 121 | 122 | double cost = 0, lastCost = 0; 123 | int nGood = 0; // good projections 124 | VecVector2d goodProjection; 125 | 126 | for (int iter = 0; iter < iterations; iter++) 127 | { 128 | nGood = 0; 129 | goodProjection.clear(); 130 | 131 | // Define Hessian and bias 132 | Matrix6d H = Matrix6d::Zero(); // 6x6 Hessian 133 | Vector6d b = Vector6d::Zero(); // 6x1 bias 134 | 135 | for (size_t i = 0; i < px_ref.size(); i++) 136 | { 137 | // compute the projection in the second image 138 | float u =px_ref[i][0], v = px_ref[i][1]; 139 | double depth = depth_ref[i]; 140 | double xc2_opt = 0,yc2_opt = 0,zc2_opt = 0; 141 | float u2_opt = 0, v2_opt = 0; 142 | 143 | double xc1 = double((u - cx)*depth/fx); 144 | double yc1 = double((v-cy)*depth/fy); 145 | Eigen::Vector3d pc1(xc1,yc1,depth); 146 | Eigen::Vector3d pc2 = T21*pc1; 147 | xc2_opt = pc2[0]; yc2_opt = pc2[1];zc2_opt = pc2[2]; 148 | u2_opt = float(fx*xc2_opt/zc2_opt+cx); 149 | v2_opt = float(fy*yc2_opt/zc2_opt+cy); 150 | 151 | if(u-4<0 || u+4>=img1.cols || v-4 <0 || v+4>=img1.rows || 152 | u2_opt-4<0 || u2_opt+4>=img2.cols || v2_opt -4<0 || v2_opt>=img2.rows) 153 | { 154 | continue; 155 | } 156 | 157 | nGood++; 158 | goodProjection.push_back(Eigen::Vector2d(u2_opt, v2_opt)); 159 | 160 | // and compute error and jacobian 161 | for (int x = -half_patch_size; x < half_patch_size; x++) 162 | for (int y = -half_patch_size; y < half_patch_size; y++) 163 | { 164 | float u1 = float(u+x), v1 = float(v+y); 165 | float u2 = float(u2_opt+x), v2 = float(v2_opt+y); 166 | 167 | double error =GetPixelValue(img1,u1,v1) - GetPixelValue(img2, u2,v2); 168 | 169 | Matrix26d J_pixel_xi = Matrix26d::Zero(); // pixel to \xi in Lie algebra 170 | Eigen::Vector2d J_img_pixel = Eigen::Vector2d::Zero(); // image gradients 171 | 172 | if(u2-1<0 || u2+1>=img2.cols || v2-1<0 || v2+1>img2.rows) 173 | { 174 | continue; 175 | } 176 | J_img_pixel[0] = (GetPixelValue(img2,u2+1,v2) - GetPixelValue(img2,u2-1,v2))/2; 177 | J_img_pixel[1] = (GetPixelValue(img2,u2,v2+1) - GetPixelValue(img2,u2,v2-1))/2; 178 | // total jacobian 179 | J_pixel_xi(0,0) = fx/zc2_opt; 180 | J_pixel_xi(0,2) = -fx * xc2_opt/(zc2_opt*zc2_opt); 181 | J_pixel_xi(0,3) = - fx * xc2_opt * yc2_opt / (zc2_opt*zc2_opt); 182 | J_pixel_xi(0,4) = fx + fx * xc2_opt*zc2_opt /(zc2_opt*zc2_opt); 183 | J_pixel_xi(0,5) = - fx * yc2_opt / zc2_opt; 184 | 185 | J_pixel_xi(1,1) = fy / zc2_opt; 186 | J_pixel_xi(1,2) = -fy * yc2_opt / (zc2_opt*zc2_opt); 187 | J_pixel_xi(1,3) = -fy - fy * yc2_opt*yc2_opt / (zc2_opt*zc2_opt); 188 | J_pixel_xi(1,4) = fy * xc2_opt * yc2_opt / (zc2_opt*zc2_opt); 189 | J_pixel_xi(1,5) = fy * xc2_opt / zc2_opt; 190 | 191 | Vector6d J = -J_pixel_xi.transpose()*J_img_pixel; 192 | 193 | 194 | H += J * J.transpose(); 195 | b += -error * J; 196 | cost += error * error; 197 | } 198 | } 199 | 200 | // solve update and put it into estimation 201 | Vector6d update; 202 | update = H.ldlt().solve(b); 203 | T21 = Sophus::SE3::exp(update) * T21; 204 | 205 | cost /= nGood; 206 | 207 | if (isnan(update[0])) 208 | { 209 | // sometimes occurred when we have a black or white patch and H is irreversible 210 | cout << "update is nan" << endl; 211 | break; 212 | } 213 | if (iter > 0 && cost > lastCost) 214 | { 215 | // cout << "cost increased: " << cost << ", " << lastCost << endl; 216 | break; 217 | } 218 | lastCost = cost; 219 | // cout << "cost = " << cost << ", good = " << nGood << endl; 220 | } 221 | // cout << "good projection: " << nGood << endl; 222 | // cout << "T21 = \n" << T21.matrix() << endl; 223 | 224 | // in order to help you debug, we plot the projected pixels here 225 | cv::Mat img1_show, img2_show; 226 | cv::cvtColor(img1, img1_show, CV_GRAY2BGR); 227 | cv::cvtColor(img2, img2_show, CV_GRAY2BGR); 228 | for (auto &px: px_ref) { 229 | cv::rectangle(img1_show, cv::Point2f(px[0] - 2, px[1] - 2), cv::Point2f(px[0] + 2, px[1] + 2), 230 | cv::Scalar(0, 250, 0)); 231 | } 232 | for (auto &px: goodProjection) { 233 | cv::rectangle(img2_show, cv::Point2f(px[0] - 2, px[1] - 2), cv::Point2f(px[0] + 2, px[1] + 2), 234 | cv::Scalar(0, 250, 0)); 235 | } 236 | cv::imshow("reference", img1_show); 237 | cv::imshow("current", img2_show); 238 | cv::waitKey(100); 239 | } 240 | 241 | void DirectPoseEstimationMultiLayer( 242 | const cv::Mat &img1, 243 | const cv::Mat &img2, 244 | const VecVector2d &px_ref, 245 | const vector depth_ref, 246 | Sophus::SE3 &T21 247 | ) { 248 | 249 | // parameters 250 | int pyramids = 4; 251 | double pyramid_scale = 0.5; 252 | double scales[] = {1.0, 0.5, 0.25, 0.125}; 253 | 254 | // create pyramids 255 | vector pyr1, pyr2; // image pyramids 256 | for(int i = 0;i= 0; level--) 267 | { 268 | VecVector2d px_ref_pyr; // set the keypoints in this pyramid level 269 | for (auto &px: px_ref) 270 | { 271 | px_ref_pyr.push_back(scales[level] * px); 272 | } 273 | 274 | // scale fx, fy, cx, cy in different pyramid levels 275 | fx = fxG*scales[level]; 276 | fy = fyG*scales[level]; 277 | cx = cxG*scales[level]; 278 | cy = cyG*scales[level]; 279 | 280 | DirectPoseEstimationSingleLayer(pyr1[level], pyr2[level], px_ref_pyr, depth_ref, T21); 281 | } 282 | } 283 | -------------------------------------------------------------------------------- /vslam_fourteen_lectures/direct_method/disparity.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/XLMaverick/Visual-Localization-Percessing/34549bcd1ec11d87b8466d3c264706a325ffdd11/vslam_fourteen_lectures/direct_method/disparity.png -------------------------------------------------------------------------------- /vslam_fourteen_lectures/direct_method/left.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/XLMaverick/Visual-Localization-Percessing/34549bcd1ec11d87b8466d3c264706a325ffdd11/vslam_fourteen_lectures/direct_method/left.png -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | #include 4 | #include 5 | #include 6 | 7 | using namespace std; 8 | using namespace Eigen; 9 | 10 | // 文件路径，如果不对，请调整 11 | string left_file = "../left.png"; 12 | string right_file = "../right.png"; 13 | string disparity_file = "../disparity.png"; 14 | 15 | // 在panglin中画图，已写好，无需调整 16 | void showPointCloud(const vector> &pointcloud); 17 | 18 | int main(int argc, char **argv) { 19 | 20 | // 内参 21 | double fx = 718.856, fy = 718.856, cx = 607.1928, cy = 185.2157; 22 | // 间距 23 | double b = 0.573; 24 | 25 | // 读取图像 26 | cv::Mat left = cv::imread(left_file, 0); 27 | cv::Mat right = cv::imread(right_file, 0); 28 | cv::Mat disparity = cv::imread(disparity_file, 0); // disparty 为CV_8U,单位为像素 29 | 30 | // 生成点云 31 | vector> pointcloud; 32 | 33 | // TODO 根据双目模型计算点云 34 | // 如果你的机器慢，请把后面的v++和u++改成v+=2, u+=2 35 | for (int v = 0; v < left.rows; v++) 36 | for (int u = 0; u < left.cols; u++) { 37 | 38 | Vector4d point(0, 0, 0, left.at(v, u) / 255.0); // 前三维为xyz,第四维为颜色 39 | 40 | // start your code here (~6 lines) 41 | // 根据双目模型计算 point 的位置 42 | uchar d= disparity.at(v,u); 43 | point[2] = (fx*b)/d; 44 | point[1] = point[2]*(v-cy)/fy; 45 | point[0] = point[2]*(u-cx)/fx; 46 | pointcloud.push_back(point); 47 | // end your code here 48 | } 49 | cout<<"pointcloud size: "<> &pointcloud) { 56 | 57 | if (pointcloud.empty()) { 58 | cerr << "Point cloud is empty!" << endl; 59 | return; 60 | } 61 | 62 | pangolin::CreateWindowAndBind("Point Cloud Viewer", 1024, 768); 63 | glEnable(GL_DEPTH_TEST); 64 | glEnable(GL_BLEND); 65 | glBlendFunc(GL_SRC_ALPHA, GL_ONE_MINUS_SRC_ALPHA); 66 | 67 | pangolin::OpenGlRenderState s_cam( 68 | pangolin::ProjectionMatrix(1024, 768, 500, 500, 512, 389, 0.1, 1000), 69 | pangolin::ModelViewLookAt(0, -0.1, -1.8, 0, 0, 0, 0.0, -1.0, 0.0) 70 | ); 71 | 72 | pangolin::View &d_cam = pangolin::CreateDisplay() 73 | .SetBounds(0.0, 1.0, pangolin::Attach::Pix(175), 1.0, -1024.0f / 768.0f) 74 | .SetHandler(new pangolin::Handler3D(s_cam)); 75 | 76 | while (pangolin::ShouldQuit() == false) { 77 | glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT); 78 | 79 | d_cam.Activate(s_cam); 80 | glClearColor(1.0f, 1.0f, 1.0f, 1.0f); 81 | 82 | glPointSize(2); 83 | glBegin(GL_POINTS); 84 | for (auto &p: pointcloud) { 85 | glColor3f(p[3], p[3], p[3]); 86 | glVertex3d(p[0], p[1], p[2]); 87 | } 88 | glEnd(); 89 | pangolin::FinishFrame(); 90 | usleep(5000); // sleep 5 ms 91 | } 92 | return; 93 | } 94 | -------------------------------------------------------------------------------- /vslam_fourteen_lectures/disparity/disparity.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/XLMaverick/Visual-Localization-Percessing/34549bcd1ec11d87b8466d3c264706a325ffdd11/vslam_fourteen_lectures/disparity/disparity.png -------------------------------------------------------------------------------- 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namespace std; 7 | using namespace Eigen; 8 | 9 | int main(int argc, char **argv) 10 | { 11 | double ar = 1.0, br = 2.0, cr = 1.0; // 真实参数值 12 | double ae = 2.0, be = -1.0, ce = 5.0; // 估计参数值 13 | int N = 100; // 数据点 14 | double w_sigma = 1.0; // 噪声Sigma值 15 | cv::RNG rng; // OpenCV随机数产生器 16 | 17 | vector x_data, y_data; // 数据 18 | for (int i = 0; i < N; i++) 19 | { 20 | double x = i / 100.0; 21 | x_data.push_back(x); 22 | y_data.push_back(exp(ar * x * x + br * x + cr) + rng.gaussian(w_sigma)); 23 | } 24 | 25 | // 开始Gauss-Newton迭代 26 | int iterations = 100; // 迭代次数 27 | double cost = 0, lastCost = 0; // 本次迭代的cost和上一次迭代的cost 28 | 29 | for (int iter = 0; iter < iterations; iter++) 30 | { 31 | Matrix3d H = Matrix3d::Zero(); // Hessian = J^T J in Gauss-Newton 32 | Vector3d b = Vector3d::Zero(); // bias 33 | cost = 0; 34 | 35 | for (int i = 0; i < N; i++) 36 | { 37 | double xi = x_data[i], yi = y_data[i]; // 第i个数据点 38 | double error = 0; // 第i个数据点的计算误差 39 | error =yi -exp(ae*xi*xi+be*xi+ce); // 填写计算error的表达式 40 | Vector3d J; // 雅可比矩阵 41 | J[0] = -exp(ae*xi*xi+be*xi+ce)*xi*xi; // de/da 42 | J[1] = -exp(ae*xi*xi+be*xi+ce)*xi; // de/db 43 | J[2] = -exp(ae*xi*xi+be*xi+ce); // de/dc 44 | 45 | H += J * J.transpose(); // GN近似的H 46 | b += -error * J; 47 | 48 | cost += error * error; 49 | } 50 | 51 | // 求解线性方程 Hx=b，建议用ldlt 52 | Vector3d dx; 53 | dx = H.ldlt().solve(b); 54 | 55 | if (isnan(dx[0])) 56 | { 57 | cout << "result is nan!" << endl; 58 | break; 59 | } 60 | 61 | if (iter > 0 && cost > lastCost) { 62 | // 误差增长了，说明近似的不够好 63 | cout << "cost: " << cost << ", last cost: " << lastCost << endl; 64 | break; 65 | } 66 | 67 | // 更新abc估计值 68 | ae += dx[0]; 69 | be += dx[1]; 70 | ce += dx[2]; 71 | 72 | lastCost = cost; 73 | 74 | cout << "total cost: " << cost << endl; 75 | } 76 | 77 | cout << "estimated abc = " << ae << ", " << be << ", " << ce << endl; 78 | return 0; 79 | } 80 | -------------------------------------------------------------------------------- /vslam_fourteen_lectures/gauessnewton/result.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/XLMaverick/Visual-Localization-Percessing/34549bcd1ec11d87b8466d3c264706a325ffdd11/vslam_fourteen_lectures/gauessnewton/result.png -------------------------------------------------------------------------------- /vslam_fourteen_lectures/icp/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | CMAKE_MINIMUM_REQUIRED(VERSION 2.8) 2 | 3 | SET(BUILD_TYPE Release) 4 | 5 | PROJECT(icp) 6 | 7 | SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 -O3") 8 | 9 | LIST(APPEND CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR}/cmake_modules ) 10 | 11 | set(Sophus_DIR /home/yxl/workspace/gaoxiangSlambook/slambook/3rdparty/Sophus/build/) 12 | FIND_PACKAGE(Sophus REQUIRED) 13 | FIND_PACKAGE(Pangolin REQUIRED) 14 | FIND_PACKAGE(G2O REQUIRED) 15 | FIND_PACKAGE(CSparse REQUIRED) 16 | 17 | INCLUDE_DIRECTORIES(${Sophus_INCLUDE_DIRS} 18 | "/usr/include/eigen3" 19 | ${Pangolin_INCLUDE_DIRS} 20 | ${G2O_INCLUDE_DIRS} 21 | ${CSPARSE_INCLUDE_DIR} 22 | ) 23 | 24 | MESSAGE(STATUS "Sophus INCLUD: ${Sophus_INCLUDE_DIRS} \n ${Sophus_LIBRARIES}") 25 | 26 | MESSAGE(STATUS "Pangolin INCLUD: ${Pangolin_INCLUDE_DIRS} \n ${Pangolin_LIBRARIES}") 27 | 28 | MESSAGE(STATUS "G2O INCLUD: ${G2O_INCLUDE_DIRS} ") 29 | 30 | 31 | ADD_EXECUTABLE(${PROJECT_NAME} icp.cpp) 32 | 33 | TARGET_LINK_LIBRARIES(${PROJECT_NAME} 34 | ${Sophus_LIBRARIES} 35 | ${Pangolin_LIBRARIES} 36 | g2o_core g2o_stuff g2o_types_sba g2o_csparse_extension 37 | ${CSPARSE_LIBRARY} 38 | ) -------------------------------------------------------------------------------- /vslam_fourteen_lectures/icp/ba_result.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/XLMaverick/Visual-Localization-Percessing/34549bcd1ec11d87b8466d3c264706a325ffdd11/vslam_fourteen_lectures/icp/ba_result.png -------------------------------------------------------------------------------- /vslam_fourteen_lectures/icp/ba_result1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/XLMaverick/Visual-Localization-Percessing/34549bcd1ec11d87b8466d3c264706a325ffdd11/vslam_fourteen_lectures/icp/ba_result1.png -------------------------------------------------------------------------------- /vslam_fourteen_lectures/icp/cmake_modules/FindCSparse.cmake: -------------------------------------------------------------------------------- 1 | # Look for csparse; note the difference in the directory specifications! 2 | FIND_PATH(CSPARSE_INCLUDE_DIR NAMES cs.h 3 | PATHS 4 | /usr/include/suitesparse 5 | /usr/include 6 | /opt/local/include 7 | /usr/local/include 8 | /sw/include 9 | /usr/include/ufsparse 10 | /opt/local/include/ufsparse 11 | /usr/local/include/ufsparse 12 | /sw/include/ufsparse 13 | ) 14 | 15 | FIND_LIBRARY(CSPARSE_LIBRARY NAMES cxsparse 16 | PATHS 17 | /usr/lib 18 | /usr/local/lib 19 | /opt/local/lib 20 | /sw/lib 21 | ) 22 | 23 | include(FindPackageHandleStandardArgs) 24 | find_package_handle_standard_args(CSPARSE DEFAULT_MSG 25 | CSPARSE_INCLUDE_DIR CSPARSE_LIBRARY) 26 | -------------------------------------------------------------------------------- /vslam_fourteen_lectures/icp/cmake_modules/FindG2O.cmake: -------------------------------------------------------------------------------- 1 | # Find the header files 2 | 3 | FIND_PATH(G2O_INCLUDE_DIR g2o/core/base_vertex.h 4 | ${G2O_ROOT}/include 5 | $ENV{G2O_ROOT}/include 6 | $ENV{G2O_ROOT} 7 | /usr/local/include 8 | /usr/include 9 | /opt/local/include 10 | /sw/local/include 11 | /sw/include 12 | NO_DEFAULT_PATH 13 | ) 14 | 15 | # Macro to unify finding both the debug and release versions of the 16 | # libraries; this is adapted from the OpenSceneGraph FIND_LIBRARY 17 | # macro. 18 | 19 | MACRO(FIND_G2O_LIBRARY MYLIBRARY MYLIBRARYNAME) 20 | 21 | FIND_LIBRARY("${MYLIBRARY}_DEBUG" 22 | NAMES "g2o_${MYLIBRARYNAME}_d" 23 | PATHS 24 | ${G2O_ROOT}/lib/Debug 25 | ${G2O_ROOT}/lib 26 | $ENV{G2O_ROOT}/lib/Debug 27 | $ENV{G2O_ROOT}/lib 28 | NO_DEFAULT_PATH 29 | ) 30 | 31 | FIND_LIBRARY("${MYLIBRARY}_DEBUG" 32 | NAMES "g2o_${MYLIBRARYNAME}_d" 33 | PATHS 34 | ~/Library/Frameworks 35 | /Library/Frameworks 36 | /usr/local/lib 37 | /usr/local/lib64 38 | /usr/lib 39 | /usr/lib64 40 | /opt/local/lib 41 | /sw/local/lib 42 | /sw/lib 43 | ) 44 | 45 | FIND_LIBRARY(${MYLIBRARY} 46 | NAMES "g2o_${MYLIBRARYNAME}" 47 | PATHS 48 | ${G2O_ROOT}/lib/Release 49 | ${G2O_ROOT}/lib 50 | $ENV{G2O_ROOT}/lib/Release 51 | $ENV{G2O_ROOT}/lib 52 | NO_DEFAULT_PATH 53 | ) 54 | 55 | FIND_LIBRARY(${MYLIBRARY} 56 | NAMES "g2o_${MYLIBRARYNAME}" 57 | PATHS 58 | ~/Library/Frameworks 59 | /Library/Frameworks 60 | /usr/local/lib 61 | /usr/local/lib64 62 | /usr/lib 63 | /usr/lib64 64 | /opt/local/lib 65 | /sw/local/lib 66 | /sw/lib 67 | ) 68 | 69 | IF(NOT ${MYLIBRARY}_DEBUG) 70 | IF(MYLIBRARY) 71 | SET(${MYLIBRARY}_DEBUG ${MYLIBRARY}) 72 | ENDIF(MYLIBRARY) 73 | ENDIF( NOT ${MYLIBRARY}_DEBUG) 74 | 75 | ENDMACRO(FIND_G2O_LIBRARY LIBRARY LIBRARYNAME) 76 | 77 | # Find the core elements 78 | FIND_G2O_LIBRARY(G2O_STUFF_LIBRARY stuff) 79 | FIND_G2O_LIBRARY(G2O_CORE_LIBRARY core) 80 | 81 | # Find the CLI library 82 | FIND_G2O_LIBRARY(G2O_CLI_LIBRARY cli) 83 | 84 | # Find the pluggable solvers 85 | FIND_G2O_LIBRARY(G2O_SOLVER_CHOLMOD solver_cholmod) 86 | FIND_G2O_LIBRARY(G2O_SOLVER_CSPARSE solver_csparse) 87 | FIND_G2O_LIBRARY(G2O_SOLVER_CSPARSE_EXTENSION csparse_extension) 88 | FIND_G2O_LIBRARY(G2O_SOLVER_DENSE solver_dense) 89 | FIND_G2O_LIBRARY(G2O_SOLVER_PCG solver_pcg) 90 | FIND_G2O_LIBRARY(G2O_SOLVER_SLAM2D_LINEAR solver_slam2d_linear) 91 | FIND_G2O_LIBRARY(G2O_SOLVER_STRUCTURE_ONLY solver_structure_only) 92 | FIND_G2O_LIBRARY(G2O_SOLVER_EIGEN solver_eigen) 93 | 94 | # Find the predefined types 95 | FIND_G2O_LIBRARY(G2O_TYPES_DATA types_data) 96 | FIND_G2O_LIBRARY(G2O_TYPES_ICP types_icp) 97 | FIND_G2O_LIBRARY(G2O_TYPES_SBA types_sba) 98 | FIND_G2O_LIBRARY(G2O_TYPES_SCLAM2D types_sclam2d) 99 | FIND_G2O_LIBRARY(G2O_TYPES_SIM3 types_sim3) 100 | FIND_G2O_LIBRARY(G2O_TYPES_SLAM2D types_slam2d) 101 | FIND_G2O_LIBRARY(G2O_TYPES_SLAM3D types_slam3d) 102 | 103 | # G2O solvers declared found if we found at least one solver 104 | SET(G2O_SOLVERS_FOUND "NO") 105 | IF(G2O_SOLVER_CHOLMOD OR G2O_SOLVER_CSPARSE OR G2O_SOLVER_DENSE OR G2O_SOLVER_PCG OR G2O_SOLVER_SLAM2D_LINEAR OR G2O_SOLVER_STRUCTURE_ONLY OR G2O_SOLVER_EIGEN) 106 | SET(G2O_SOLVERS_FOUND "YES") 107 | ENDIF(G2O_SOLVER_CHOLMOD OR G2O_SOLVER_CSPARSE OR G2O_SOLVER_DENSE OR G2O_SOLVER_PCG OR G2O_SOLVER_SLAM2D_LINEAR OR G2O_SOLVER_STRUCTURE_ONLY OR G2O_SOLVER_EIGEN) 108 | 109 | # G2O itself declared found if we found the core libraries and at least one solver 110 | SET(G2O_FOUND "NO") 111 | IF(G2O_STUFF_LIBRARY AND G2O_CORE_LIBRARY AND G2O_INCLUDE_DIR AND G2O_SOLVERS_FOUND) 112 | SET(G2O_FOUND "YES") 113 | ENDIF(G2O_STUFF_LIBRARY AND G2O_CORE_LIBRARY AND G2O_INCLUDE_DIR AND G2O_SOLVERS_FOUND) 114 | -------------------------------------------------------------------------------- /vslam_fourteen_lectures/icp/icp.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | #include 4 | #include 5 | #include 6 | #include 7 | #include 8 | #include 9 | 10 | 11 | #include 12 | #include 13 | #include 14 | #include 15 | #include 16 | #include 17 | #include 18 | 19 | 20 | 21 | using namespace std; 22 | using namespace Eigen; 23 | 24 | 25 | string trajectory_file = "../compare.txt"; 26 | 27 | 28 | 29 | 30 | double timestamp_g ,timestamp_e; 31 | Eigen::Vector3d tg, te; 32 | Eigen::Quaterniond qg, qe; 33 | Sophus::SE3 Tg , Te; 34 | 35 | 36 | // start point is red and end point is blue 37 | void DrawTrajectory(vector> pose, vector> pose2); 38 | 39 | void pose_estimation_3d3d (const vector& pts1, 40 | const vector& pts2, 41 | Eigen::Matrix3d& R, Eigen::Vector3d& t 42 | ); 43 | 44 | void bundleAdjustment ( 45 | const vector< Eigen::Vector3d>& pts1, 46 | const vector< Eigen::Vector3d >& pts2, 47 | Eigen::Matrix3d& R, Eigen::Vector3d& t ); 48 | 49 | 50 | int main(int argc, char **argv) 51 | { 52 | vector> poses_g, poses_e, poses_c; 53 | 54 | vector position_g, position_e; 55 | ifstream trajectory; 56 | trajectory.open(trajectory_file.c_str()); 57 | 58 | if(!trajectory) 59 | { 60 | cout<<"open file error!"<>timestamp_e>>te[0]>>te[1]>>te[2]>>qe.x()>>qe.y()>>qe.z()>>qe.w() 68 | >>timestamp_g>>tg[0]>>tg[1]>>tg[2]>>qg.x()>>qg.y()>>qg.z()>>qg.w(); 69 | Te = Sophus::SE3(qe,te); 70 | Tg = Sophus::SE3(qg,tg); 71 | position_g.push_back(tg); 72 | position_e.push_back(te); 73 | poses_g.push_back(Tg); 74 | poses_e.push_back(Te); 75 | } 76 | trajectory.close(); 77 | // DrawTrajectory(poses_g, poses_e); 78 | 79 | Eigen::Matrix3d R = Eigen::Matrix3d::Identity(); 80 | Eigen::Vector3d t = Eigen::Vector3d::Zero(); 81 | 82 | // pose_estimation_3d3d(position_g, position_e, R,t); 83 | bundleAdjustment(position_g, position_e, R,t); 84 | 85 | Sophus::SE3 Tge(R,t); 86 | 87 | // cout<<"the T is "< 99 | { 100 | public: 101 | EIGEN_MAKE_ALIGNED_OPERATOR_NEW; 102 | EdgeProjectXYZRGBDPoseOnly( const Eigen::Vector3d& point ) : _point(point) {} 103 | 104 | virtual void computeError() 105 | { 106 | const g2o::VertexSE3Expmap* pose = static_cast ( _vertices[0] ); 107 | // measurement is p, point is p' 108 | _error = _measurement - pose->estimate().map( _point ); 109 | } 110 | 111 | virtual void linearizeOplus() 112 | { 113 | g2o::VertexSE3Expmap* pose = static_cast(_vertices[0]); 114 | g2o::SE3Quat T(pose->estimate()); 115 | Eigen::Vector3d xyz_trans = T.map(_point); 116 | double x = xyz_trans[0]; 117 | double y = xyz_trans[1]; 118 | double z = xyz_trans[2]; 119 | 120 | _jacobianOplusXi(0,0) = 0; 121 | _jacobianOplusXi(0,1) = -z; 122 | _jacobianOplusXi(0,2) = y; 123 | _jacobianOplusXi(0,3) = -1; 124 | _jacobianOplusXi(0,4) = 0; 125 | _jacobianOplusXi(0,5) = 0; 126 | 127 | _jacobianOplusXi(1,0) = z; 128 | _jacobianOplusXi(1,1) = 0; 129 | _jacobianOplusXi(1,2) = -x; 130 | _jacobianOplusXi(1,3) = 0; 131 | _jacobianOplusXi(1,4) = -1; 132 | _jacobianOplusXi(1,5) = 0; 133 | 134 | _jacobianOplusXi(2,0) = -y; 135 | _jacobianOplusXi(2,1) = x; 136 | _jacobianOplusXi(2,2) = 0; 137 | _jacobianOplusXi(2,3) = 0; 138 | _jacobianOplusXi(2,4) = 0; 139 | _jacobianOplusXi(2,5) = -1; 140 | } 141 | 142 | bool read ( istream& in ) {} 143 | bool write ( ostream& out ) const {} 144 | protected: 145 | Eigen::Vector3d _point; 146 | }; 147 | 148 | 149 | void bundleAdjustment ( 150 | const vector< Eigen::Vector3d>& pts1, 151 | const vector< Eigen::Vector3d >& pts2, 152 | Eigen::Matrix3d& R, Eigen::Vector3d& t ) 153 | { 154 | // 初始化g2o 155 | typedef g2o::BlockSolver< g2o::BlockSolverTraits<6,3> > Block; // pose维度为 6, landmark 维度为 3 156 | Block::LinearSolverType* linearSolver = new g2o::LinearSolverEigen(); // 线性方程求解器 157 | Block* solver_ptr = new Block( linearSolver ); // 矩阵块求解器 158 | g2o::OptimizationAlgorithmGaussNewton* solver = new g2o::OptimizationAlgorithmGaussNewton( solver_ptr ); 159 | g2o::SparseOptimizer optimizer; 160 | optimizer.setAlgorithm( solver ); 161 | 162 | // vertex 163 | g2o::VertexSE3Expmap* pose = new g2o::VertexSE3Expmap(); // camera pose 164 | pose->setId(0); 165 | pose->setEstimate( g2o::SE3Quat( 166 | Eigen::Matrix3d::Identity(), 167 | Eigen::Vector3d( 0,0,0 ) 168 | ) ); 169 | optimizer.addVertex( pose ); 170 | 171 | // edges 172 | int index = 1; 173 | vector edges; 174 | for ( size_t i=0; isetId( index ); 178 | edge->setVertex( 0, dynamic_cast (pose) ); 179 | edge->setMeasurement( pts1[i]); 180 | edge->setInformation( Eigen::Matrix3d::Identity()*1e4 ); 181 | optimizer.addEdge(edge); 182 | index++; 183 | edges.push_back(edge); 184 | } 185 | 186 | // chrono::steady_clock::time_point t1 = chrono::steady_clock::now(); 187 | optimizer.setVerbose( true ); //可以打印出优化过程信息 188 | optimizer.initializeOptimization(); 189 | optimizer.optimize(10); 190 | // chrono::steady_clock::time_point t2 = chrono::steady_clock::now(); 191 | // chrono::duration time_used = chrono::duration_cast>(t2-t1); 192 | // cout<<"optimization costs time: "< 2 | #include 3 | #include 4 | #include 5 | 6 | using namespace std; 7 | using namespace cv; 8 | 9 | // this program shows how to use optical flow 10 | 11 | string file_1 = "../1.png"; // first image 12 | string file_2 = "../2.png"; // second image 13 | 14 | // TODO implement this funciton 15 | /** 16 | * single level optical flow 17 | * @param [in] img1 the first image 18 | * @param [in] img2 the second image 19 | * @param [in] kp1 keypoints in img1 20 | * @param [in|out] kp2 keypoints in img2, if empty, use initial guess in kp1 21 | * @param [out] success true if a keypoint is tracked successfully 22 | * @param [in] inverse use inverse formulation? 23 | */ 24 | void OpticalFlowSingleLevel( 25 | const Mat &img1, 26 | const Mat &img2, 27 | const vector &kp1, 28 | vector &kp2, 29 | vector &success, 30 | bool inverse = true 31 | ); 32 | 33 | // TODO implement this funciton 34 | /** 35 | * multi level optical flow, scale of pyramid is set to 2 by default 36 | * the image pyramid will be create inside the function 37 | * @param [in] img1 the first pyramid 38 | * @param [in] img2 the second pyramid 39 | * @param [in] kp1 keypoints in img1 40 | * @param [out] kp2 keypoints in img2 41 | * @param [out] success true if a keypoint is tracked successfully 42 | * @param [in] inverse set true to enable inverse formulation 43 | */ 44 | void OpticalFlowMultiLevel( 45 | const Mat &img1, 46 | const Mat &img2, 47 | const vector &kp1, 48 | vector &kp2, 49 | vector &success, 50 | bool inverse = false 51 | ); 52 | 53 | /** 54 | * get a gray scale value from reference image (bi-linear interpolated) 55 | * @param img 56 | * @param x 57 | * @param y 58 | * @return 59 | */ 60 | inline float GetPixelValue(const cv::Mat &img, float x, float y) 61 | { 62 | uchar *data = &img.data[int(y) * img.step + int(x)]; 63 | float xx = x - floor(x); 64 | float yy = y - floor(y); 65 | return float( 66 | (1 - xx) * (1 - yy) * data[0] + 67 | xx * (1 - yy) * data[1] + 68 | (1 - xx) * yy * data[img.step] + 69 | xx * yy * data[img.step + 1] 70 | ); 71 | } 72 | 73 | 74 | int main(int argc, char **argv) { 75 | 76 | // images, note they are CV_8UC1, not CV_8UC3 77 | Mat img1 = imread(file_1, 0); 78 | Mat img2 = imread(file_2, 0); 79 | 80 | // key points, using GFTT here. 81 | vector kp1; 82 | Ptr detector = GFTTDetector::create(500, 0.01, 20); // maximum 500 keypoints 83 | detector->detect(img1, kp1); 84 | 85 | // now lets track these key points in the second image 86 | // first use single level LK in the validation picture 87 | vector kp2_single; 88 | vector success_single; 89 | OpticalFlowSingleLevel(img1, img2, kp1, kp2_single, success_single); 90 | 91 | // then test multi-level LK 92 | vector kp2_multi; 93 | vector success_multi; 94 | OpticalFlowMultiLevel(img1, img2, kp1, kp2_multi, success_multi); 95 | 96 | // use opencv's flow for validation 97 | vector pt1, pt2; 98 | for (auto &kp: kp1) pt1.push_back(kp.pt); 99 | vector status; 100 | vector error; 101 | cv::calcOpticalFlowPyrLK(img1, img2, pt1, pt2, status, error, cv::Size(8, 8)); 102 | 103 | // plot the differences of those functions 104 | Mat img2_single; 105 | cv::cvtColor(img2, img2_single, CV_GRAY2BGR); 106 | for (int i = 0; i < kp2_single.size(); i++) { 107 | if (success_single[i]) { 108 | cv::circle(img2_single, kp2_single[i].pt, 2, cv::Scalar(0, 250, 0), 2); 109 | cv::line(img2_single, kp1[i].pt, kp2_single[i].pt, cv::Scalar(0, 250, 0)); 110 | } 111 | } 112 | 113 | Mat img2_multi; 114 | cv::cvtColor(img2, img2_multi, CV_GRAY2BGR); 115 | for (int i = 0; i < kp2_multi.size(); i++) { 116 | if (success_multi[i]) { 117 | cv::circle(img2_multi, kp2_multi[i].pt, 2, cv::Scalar(0, 250, 0), 2); 118 | cv::line(img2_multi, kp1[i].pt, kp2_multi[i].pt, cv::Scalar(0, 250, 0)); 119 | } 120 | } 121 | 122 | Mat img2_CV; 123 | cv::cvtColor(img2, img2_CV, CV_GRAY2BGR); 124 | for (int i = 0; i < pt2.size(); i++) { 125 | if (status[i]) { 126 | cv::circle(img2_CV, pt2[i], 2, cv::Scalar(0, 250, 0), 2); 127 | cv::line(img2_CV, pt1[i], pt2[i], cv::Scalar(0, 250, 0)); 128 | } 129 | } 130 | 131 | cv::imshow("tracked single level", img2_single); 132 | cv::imwrite("../single_i.png", img2_single); 133 | cv::imshow("tracked multi level", img2_multi); 134 | cv::imwrite("../multi_i.png", img2_multi); 135 | cv::imshow("tracked by opencv", img2_CV); 136 | cv::imwrite("../opencv_op.png", img2_CV); 137 | cv::waitKey(0); 138 | 139 | return 0; 140 | } 141 | 142 | void OpticalFlowSingleLevel( 143 | const Mat &img1, 144 | const Mat &img2, 145 | const vector &kp1, 146 | vector &kp2, 147 | vector &success, 148 | bool inverse 149 | ) { 150 | 151 | // parameters 152 | int half_patch_size = 4; 153 | int iterations = 10; 154 | bool have_initial = !kp2.empty(); 155 | 156 | for (size_t i = 0; i < kp1.size(); i++) 157 | { 158 | auto kp = kp1[i]; 159 | double dx = 0, dy = 0; // dx,dy need to be estimated 160 | if (have_initial) 161 | { 162 | dx = kp2[i].pt.x - kp.pt.x; 163 | dy = kp2[i].pt.y - kp.pt.y; 164 | } 165 | 166 | double cost = 0, lastCost = 0; 167 | bool succ = true; // indicate if this point succeeded 168 | 169 | // Gauss-Newton iterations 170 | for (int iter = 0; iter < iterations; iter++) 171 | { 172 | Eigen::Matrix2d H = Eigen::Matrix2d::Zero(); 173 | Eigen::Vector2d b = Eigen::Vector2d::Zero(); 174 | cost = 0; 175 | 176 | if (kp.pt.x + dx <= half_patch_size || kp.pt.x + dx >= img1.cols - half_patch_size || 177 | kp.pt.y + dy <= half_patch_size || kp.pt.y + dy >= img1.rows - half_patch_size) 178 | { // go outside 179 | succ = false; 180 | break; 181 | } 182 | 183 | // compute cost and jacobian 184 | for (int x = -half_patch_size; x < half_patch_size; x++) 185 | for (int y = -half_patch_size; y < half_patch_size; y++) 186 | { 187 | double error = 0; 188 | float u1 = float(kp.pt.x + x), v1 = float(kp.pt.y+y); 189 | float u2 = float(u1+dx), v2 = float(v1+dy); 190 | Eigen::Vector2d J; // Jacobian 191 | if (inverse == false) 192 | { 193 | J.x() = double(GetPixelValue(img2,u2+1,v2) - GetPixelValue(img2,u2-1,v2))/2; 194 | J.y() = double(GetPixelValue(img2,u2,v2+1) - GetPixelValue(img2,u2,v2-1))/2; 195 | error = double(GetPixelValue(img2,u2,v2) - GetPixelValue(img1,u1,v1)); 196 | // Forward Jacobian 197 | } else 198 | { 199 | // Inverse Jacobian 200 | // NOTE this J does not change when dx, dy is updated, so we can store it and only compute error 201 | J.x() = double(GetPixelValue(img1,u1+1,v1) - GetPixelValue(img1,u1-1,v1))/2; 202 | J.y() = double(GetPixelValue(img1,u1,v1+1) - GetPixelValue(img1,u1,v1-1))/2; 203 | error = double(GetPixelValue(img2,u2,v2) - GetPixelValue(img1,u1,v1)); 204 | } 205 | 206 | H += J*J.transpose(); 207 | b += -J*error; 208 | cost += error*error; 209 | } 210 | 211 | // compute update 212 | Eigen::Vector2d update; 213 | update = H.ldlt().solve(b); 214 | 215 | if (isnan(update[0])) 216 | { 217 | // sometimes occurred when we have a black or white patch and H is irreversible 218 | cout << "update is nan" << endl; 219 | succ = false; 220 | break; 221 | } 222 | if (iter > 0 && cost > lastCost) 223 | { 224 | cout << "cost increased: " << cost << ", " << lastCost << endl; 225 | break; 226 | } 227 | 228 | // update dx, dy 229 | dx += update[0]; 230 | dy += update[1]; 231 | lastCost = cost; 232 | succ = true; 233 | } 234 | success.push_back(succ); 235 | // set kp2 236 | if (have_initial) 237 | { 238 | kp2[i].pt = kp.pt + Point2f(dx, dy); 239 | } else 240 | { 241 | KeyPoint tracked = kp; 242 | tracked.pt += cv::Point2f(dx, dy); 243 | kp2.push_back(tracked); 244 | } 245 | } 246 | } 247 | 248 | void OpticalFlowMultiLevel( 249 | const Mat &img1, 250 | const Mat &img2, 251 | const vector &kp1, 252 | vector &kp2, 253 | vector &success, 254 | bool inverse) { 255 | 256 | // parameters 257 | int pyramids = 4; 258 | double pyramid_scale = 0.5; 259 | double scales[] = {1.0, 0.5, 0.25, 0.125}; 260 | 261 | // create pyramids 262 | vector pyr1, pyr2; // image pyramids 263 | // TODO START YOUR CODE HERE (~8 lines) 264 | for (int i = 0; i < pyramids; i++) 265 | { 266 | Mat img1_temp, img2_temp; 267 | resize(img1, img1_temp,Size(img1.cols*scales[i], img1.rows*scales[i])); 268 | resize(img2, img2_temp,Size(img2.cols*scales[i], img2.rows*scales[i])); 269 | pyr1.push_back(img1_temp); 270 | pyr2.push_back(img2_temp); 271 | cout<<"Pyramid"< vkp2_now; 275 | vector vkp2_last; 276 | vector vsucc; 277 | for(int i = pyramids-1;i>=0;i--) 278 | { 279 | vector vkp1; 280 | for(int j = 0; j 2 | #include 3 | #include 4 | #include 5 | 6 | // need pangolin for plotting trajectory 7 | #include 8 | 9 | using namespace std; 10 | 11 | // path to trajectory file 12 | string trajectory_file_g = "../groundtruth.txt"; 13 | double timestamp_g; 14 | Eigen::Vector3d tg; 15 | Eigen::Quaterniond qg; 16 | Sophus::SE3 Tg; 17 | 18 | 19 | string trajectory_file_e = "../estimated.txt"; 20 | double timestamp_e; 21 | Eigen::Vector3d te; 22 | Eigen::Quaterniond qe; 23 | Sophus::SE3 Te; 24 | 25 | Eigen::Matrix kesi; 26 | double error = 0; 27 | double RMSE; 28 | 29 | int main(int argc, char **argv) 30 | { 31 | // vector> poses; 32 | 33 | ifstream trajectory_g; 34 | ifstream trajectory_e; 35 | trajectory_g.open(trajectory_file_g.c_str()); 36 | trajectory_e.open(trajectory_file_e.c_str()); 37 | 38 | if(!trajectory_g || ! trajectory_e) 39 | { 40 | cout<<"open file error!"<>timestamp_g>>tg[0]>>tg[1]>>tg[2]>>qg.x()>>qg.y()>>qg.z()>>qg.w(); 52 | templine_e>>timestamp_e>>te[0]>>te[1]>>te[2]>>qe.x()>>qe.y()>>qe.z()>>qe.w(); 53 | Tg = Sophus::SE3(qg,tg); 54 | Te = Sophus::SE3(qe,te); 55 | 56 | kesi = (Tg.inverse()*Te).log(); 57 | error += kesi.transpose()*kesi; 58 | num++; 59 | } 60 | trajectory_g.close(); 61 | trajectory_e.close(); 62 | RMSE = sqrt(error/num); 63 | cout<<"RMSE: "< 2 | #include 3 | #include 4 | #include 5 | 6 | // need pangolin for plotting trajectory 7 | #include 8 | 9 | using namespace std; 10 | 11 | // path to trajectory file 12 | string trajectory_file = "../trajectory.txt"; 13 | double timestamp; 14 | Eigen::Vector3d t; 15 | Eigen::Quaterniond q; 16 | Sophus::SE3 T; 17 | ifstream ifTraject; 18 | 19 | // function for plotting trajectory, don't edit this code 20 | // start point is red and end point is blue 21 | void DrawTrajectory(vector>); 22 | 23 | int main(int argc, char **argv) 24 | { 25 | vector> poses; 26 | 27 | ifstream trajectory; 28 | trajectory.open(trajectory_file.c_str()); 29 | 30 | if(!trajectory) 31 | { 32 | cout<<"open file error!"<>timestamp>>t[0]>>t[1]>>t[2]>>q.x()>>q.y()>>q.z()>>q.w(); 40 | T = Sophus::SE3(q,t); 41 | poses.push_back(T); 42 | } 43 | trajectory.close(); 44 | DrawTrajectory(poses); 45 | return 0; 46 | } 47 | 48 | /*******************************************************************************************/ 49 | void DrawTrajectory(vector> poses) { 50 | if (poses.empty()) { 51 | cerr << "Trajectory is empty!" << endl; 52 | return; 53 | } 54 | 55 | // create pangolin window and plot the trajectory 56 | pangolin::CreateWindowAndBind("Trajectory Viewer", 1024, 768); 57 | glEnable(GL_DEPTH_TEST); 58 | glEnable(GL_BLEND); 59 | glBlendFunc(GL_SRC_ALPHA, GL_ONE_MINUS_SRC_ALPHA); 60 | 61 | pangolin::OpenGlRenderState s_cam( 62 | pangolin::ProjectionMatrix(1024, 768, 500, 500, 512, 389, 0.1, 1000), 63 | pangolin::ModelViewLookAt(0, -0.1, -1.8, 0, 0, 0, 0.0, -1.0, 0.0) 64 | ); 65 | 66 | pangolin::View &d_cam = pangolin::CreateDisplay() 67 | .SetBounds(0.0, 1.0, pangolin::Attach::Pix(175), 1.0, -1024.0f / 768.0f) 68 | .SetHandler(new pangolin::Handler3D(s_cam)); 69 | 70 | 71 | while (pangolin::ShouldQuit() == false) { 72 | glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT); 73 | 74 | d_cam.Activate(s_cam); 75 | glClearColor(1.0f, 1.0f, 1.0f, 1.0f); 76 | 77 | glLineWidth(2); 78 | for (size_t i = 0; i < poses.size() - 1; i++) { 79 | glColor3f(1 - (float) i / poses.size(), 0.0f, (float) i / poses.size()); 80 | glBegin(GL_LINES); 81 | auto p1 = poses[i], p2 = poses[i + 1]; 82 | glVertex3d(p1.translation()[0], p1.translation()[1], p1.translation()[2]); 83 | glVertex3d(p2.translation()[0], p2.translation()[1], p2.translation()[2]); 84 | glEnd(); 85 | } 86 | pangolin::FinishFrame(); 87 | usleep(5000); // sleep 5 ms 88 | } 89 | 90 | } -------------------------------------------------------------------------------- /vslam_fourteen_lectures/undistort/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | CMAKE_MINIMUM_REQUIRED(VERSION 2.8) 2 | PROJECT(undistort) 3 | set(CMAKE_CXX_FLAGS "-std=c++11") 4 | 5 | FIND_PACKAGE(OpenCV REQUIRED) 6 | INCLUDE_DIRECTORIES(${OpenCV_INCLUDE_DIRS}) 7 | 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/vslam_fourteen_lectures/undistort/undistort_image.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | 4 | using namespace std; 5 | 6 | string image_file = "../test.png"; // 请确保路径正确 7 | 8 | int main(int argc, char **argv) { 9 | 10 | // 本程序需要你自己实现去畸变部分的代码。尽管我们可以调用OpenCV的去畸变，但自己实现一遍有助于理解。 11 | // 畸变参数 12 | double k1 = -0.28340811, k2 = 0.07395907, p1 = 0.00019359, p2 = 1.76187114e-05; 13 | // 内参 14 | double fx = 458.654, fy = 457.296, cx = 367.215, cy = 248.375; 15 | 16 | cv::Mat image = cv::imread(image_file,0); // 图像是灰度图，CV_8UC1 17 | int rows = image.rows, cols = image.cols; 18 | cv::Mat image_undistort = cv::Mat(rows, cols, CV_8UC1); // 去畸变以后的图 19 | 20 | // 计算去畸变后图像的内容 21 | for (int v = 0; v < rows; v++) 22 | { 23 | for (int u = 0; u < cols; u++) 24 | { 25 | 26 | double u_distorted = 0, v_distorted = 0; 27 | // TODO 按照公式，计算点(u,v)对应到畸变图像中的坐标(u_distorted, v_distorted) (~6 lines) 28 | // start your code here 29 | double x = (u-cx)/fx; 30 | double y = (v-cy)/fy; 31 | 32 | double r2 = x*x+y*y; 33 | double r4 = r2*r2; 34 | 35 | double x_distorted = x*(1+k1*r2+k2*r4)+2*p1*x*y+p2*(r2+2*x*x); 36 | double y_distorted = y*(1+k1*r2+k2*r4)+p1*(r2+2*y*y)+2*p2*x*y; 37 | 38 | u_distorted = fx*x_distorted+cx; 39 | v_distorted = fy*y_distorted+cy; 40 | 41 | // end your code here 42 | 43 | // 赋值 (最近邻插值) 44 | if (u_distorted >= 0 && v_distorted >= 0 && u_distorted < cols && v_distorted < rows) 45 | { 46 | image_undistort.at(v, u) = image.at((int) v_distorted, (int) u_distorted); 47 | } 48 | else 49 | { 50 | image_undistort.at(v, u) = 0; 51 | } 52 | } 53 | } 54 | 55 | // 画图去畸变后图像 56 | cv::imwrite("../result.png", image_undistort); 57 | cv::imshow("image undistorted", image_undistort); 58 | cv::waitKey(); 59 | 60 | return 0; 61 | } 62 | --------------------------------------------------------------------------------