├── CMakeLists.txt ├── LICENSE ├── README.md ├── config ├── config_eth.json └── config_kitti.json ├── data ├── ETH_TLS │ └── facade │ │ ├── s1_v0.1_sor.pcd │ │ ├── s2-s1.pose │ │ ├── s2-s3.pose │ │ ├── s2_s1_top3.match │ │ ├── s2_s3_top3.match │ │ ├── s2_v0.1_sor.pcd │ │ ├── s3-s1.pose │ │ ├── s3_s1_top3.match │ │ └── s3_v0.1_sor.pcd └── KITTI │ └── 09 │ ├── 1380_v0.3.pcd │ ├── 1391-1380.pose │ ├── 1391_1380_top3.match │ ├── 1391_v0.3.pcd │ ├── 191_v0.3.pcd │ ├── 210-191.pose │ ├── 210_191_top3.match │ ├── 210_v0.3.pcd │ ├── 821_v0.3.pcd │ ├── 833-821.pose │ ├── 833_821_top3.match │ └── 833_v0.3.pcd ├── demo.cpp ├── figures ├── framework-a.png └── framework-b.png ├── registration ├── CMakeLists.txt ├── irls_welsch.cpp ├── irls_welsch.h ├── ransac_1pt2pt3pt.cpp ├── ransac_1pt2pt3pt.h ├── tcf.cpp └── tcf.h └── utils ├── CMakeLists.txt ├── for_cloud.cpp ├── for_cloud.h ├── for_io.h ├── for_misc.h └── for_time.h /CMakeLists.txt: -------------------------------------------------------------------------------- 1 | # CMake 2 | cmake_minimum_required(VERSION 3.0) 3 | project(tcf) 4 | 5 | # C++ 6 | set(CMAKE_CXX_STANDARD 17) 7 | include_directories(${CMAKE_CURRENT_SOURCE_DIR}) 8 | add_definitions(-std=c++17 -g) 9 | set(CMAKE_BUILD_TYPE "Release") 10 | 11 | # multiple PCL exists, we select pcl-1.12 12 | find_package(PCL 1.12 REQUIRED) 13 | set(PCL_INCLUDE_DIRS /usr/local/include/pcl-1.12) 14 | # find_package(PCL REQUIRED) 15 | include_directories(${PCL_INCLUDE_DIRS}) 16 | link_directories(${PCL_LIBRARY_DIRS}) 17 | add_definitions(${PCL_DEFINITIONS}) 18 | 19 | # nlohmann_json 20 | find_package(nlohmann_json REQUIRED) 21 | 22 | add_subdirectory(utils) 23 | add_subdirectory(registration) 24 | 25 | add_executable(demo demo.cpp) 26 | target_link_libraries(demo PRIVATE ${PCL_LIBRARIES} 27 | ${TCF_REGISTRATION_LIB} ${TCF_UTILS_LIB} nlohmann_json::nlohmann_json) -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2024 Pengcheng Shi 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 |

TCF (Two-stage Consensus Filtering)

2 | 3 |

4 | TCF is a 3D correspondence-based point cloud registration method. It elevates RANSAC to SOTA speed and accuracy. Firstly, one-point RANSAC obtains a consensus set based on length consistency. Subsequently, two-point RANSAC refines the set via angle consistency. Then, three-point RANSAC computes a coarse pose and removes outliers based on transformed correspondence’s distances. Drawing on optimizations from one-point and two-point RANSAC, three-point RANSAC requires only a few iterations. Eventually, an iterative reweighted least squares (IRLS) is applied to yield the optimal pose. 5 |

6 | 7 |

8 | Subfigure 1
9 | (a) Overall framework 10 |

11 |

12 | Subfigure 2
13 | (b) Outlier Removal 14 |

15 | 16 | ## News 17 | - **2025.01:** The preprocessed dataset in TCF is available: [Dataset Available](https://drive.google.com/drive/folders/1uKN2pqymFc85tY13Cw7kUNop7uQOEOSB). 18 | - **2024.11:** TCF has been accepted for *IEEE Robotics and Automation Letters*: [Published Paper](https://ieeexplore.ieee.org/document/10758239). 19 | 20 | ## Citation 21 | If you use code or data of TCF in your academic research, please cite our paper: 22 | ``` 23 | @ARTICLE{10758239, 24 | author={Shi, Pengcheng and Yan, Shaocheng and Xiao, Yilin and Liu, Xinyi and Zhang, Yongjun and Li, Jiayuan}, 25 | journal={IEEE Robotics and Automation Letters}, 26 | title={RANSAC Back to SOTA: A Two-Stage Consensus Filtering for Real-Time 3D Registration}, 27 | year={2024}, 28 | volume={9}, 29 | number={12}, 30 | pages={11881-11888}, 31 | doi={10.1109/LRA.2024.3502056}} 32 | ``` 33 | 34 | ## Dependencies 35 | - **CMake** 36 | - **PCL** (Point Cloud Library) 37 | - **Eigen ≥ 3.4** (for slicing matrix) 38 | - **nlohmann_json** (for reading config files) 39 | 40 | ## File and data structure 41 | ### Configure files 42 | ```bash 43 | config 44 | │ ├── config_eth.json 45 | │ └── config_kitti.json 46 | ``` 47 | ### Data structure 48 | ```bash 49 | data 50 | ├── ETH_TLS 51 | │ └── facade 52 | │ ├── s1_v0.1_sor.pcd 53 | │ ├── s2_v0.1_sor.pcd 54 | │ ├── s2_s1_top3.match 55 | │ ├── s2-s1.pose 56 | │ └── ... 57 | ├── KITTI 58 | │ └── 09 59 | │ ├── 210_v0.3.pcd 60 | │ ├── 191_v0.3.pcd 61 | │ ├── 210_191_top3.match 62 | │ ├── 210-191.pose 63 | │ └── ... 64 | ``` 65 | - **xx.json**: Contains paths for source point cloud, target point cloud, initial correspondences, and ground truth pose. 66 | - **xx.pcd**: Source and target point cloud. 67 | - **A_B_top3.match**: A correspondence file where A is source (left 3 columns) and B is target (right 3 columns). 68 | - **xx.pose**: The 4*4 groundtruth pose from A to B 69 | 70 | **Note:** Source and target point clouds are used to calculate resolution and estimate noise level, enabling **a genralized method without manual parameter adjustments**. If not available, values can be set manually, but correspondences remain necessary. 71 | 72 | ## How to use? 73 | ### Complie and run Demo 74 | ```bash 75 | mkdir build && cd build 76 | cmake .. 77 | make -j8 78 | ./demo 79 | ``` 80 | ### Run other data 81 | To test with different data, simply update the data path in the config file. -------------------------------------------------------------------------------- /config/config_eth.json: -------------------------------------------------------------------------------- 1 | { 2 | "path_source_cloud": "../data/ETH_TLS/facade/s3_v0.1_sor.pcd", 3 | "path_target_cloud": "../data/ETH_TLS/facade/s1_v0.1_sor.pcd", 4 | "path_matches": "../data/ETH_TLS/facade/s3_s1_top3.match", 5 | "path_gt": "../data/ETH_TLS/facade/s3-s1.pose" 6 | } -------------------------------------------------------------------------------- /config/config_kitti.json: -------------------------------------------------------------------------------- 1 | { 2 | "path_source_cloud": "../data/KITTI/09/1391_v0.3.pcd", 3 | "path_target_cloud": "../data/KITTI/09/1380_v0.3.pcd", 4 | "path_matches": "../data/KITTI/09/1391_1380_top3.match", 5 | "path_gt": "../data/KITTI/09/1391-1380.pose" 6 | } -------------------------------------------------------------------------------- /data/ETH_TLS/facade/s1_v0.1_sor.pcd: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ShiPC-AI/TCF/401a80a332a536f53722172baa658001d7d2fb0c/data/ETH_TLS/facade/s1_v0.1_sor.pcd -------------------------------------------------------------------------------- /data/ETH_TLS/facade/s2-s1.pose: -------------------------------------------------------------------------------- 1 | 9.88035971e-001 1.54222655e-001 5.41374166e-004 5.34167810e+000 2 | -1.54223348e-001 9.88034647e-001 1.64157616e-003 -2.56153095e+000 3 | -2.81728199e-004 -1.70542883e-003 9.99998506e-001 -1.46640894e+000 4 | 0.00000000e+000 0.00000000e+000 0.00000000e+000 1.00000000e+000 5 | -------------------------------------------------------------------------------- /data/ETH_TLS/facade/s2-s3.pose: -------------------------------------------------------------------------------- 1 | 0.9990999988 -0.04241652512 -0.0001790117498 5.515607757 2 | 0.04241671735 0.999099217 0.001255197356 -5.664602064 3 | 0.0001256093891 -0.001261660767 0.9999991966 0.4792181953 4 | 0 0 0 1 5 | -------------------------------------------------------------------------------- /data/ETH_TLS/facade/s2_v0.1_sor.pcd: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ShiPC-AI/TCF/401a80a332a536f53722172baa658001d7d2fb0c/data/ETH_TLS/facade/s2_v0.1_sor.pcd -------------------------------------------------------------------------------- /data/ETH_TLS/facade/s3-s1.pose: -------------------------------------------------------------------------------- 1 | 9.80605051e-001 1.95993656e-001 4.70903652e-004 1.04304568e+000 2 | -1.95993837e-001 9.80605055e-001 3.75638389e-004 4.07405159e+000 3 | -3.88147761e-004 -4.60647115e-004 9.99999819e-001 -1.94609556e+000 4 | 0.00000000e+000 0.00000000e+000 0.00000000e+000 1.00000000e+000 5 | -------------------------------------------------------------------------------- /data/ETH_TLS/facade/s3_s1_top3.match: -------------------------------------------------------------------------------- 1 | 18.42466545105 -25.75684738159 1.517593502998 43.55418395996 -6.166964054108 4.980620861053 2 | 18.96743392944 -24.7885761261 1.595000982285 43.88135147095 -5.559737205505 5.288817882538 3 | 10.01039123535 5.210813999176 4.551418304443 11.71676921844 7.524000167847 2.071105957031 4 | 10.01039123535 5.210813999176 4.551418304443 13.2015953064 11.71345043182 -0.5314030051231 5 | -7.674449920654 9.662518501282 4.509816646576 11.14647769928 22.46021270752 0.7055970430374 6 | -6.299007892609 10.07803916931 4.5068359375 4.665719985962 2.722752571106 0.07928811758757 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-0.5859901309013 1.44982278347 12.39854335785 12.98394012451 -0.1259769946337 1889 | 3.398679018021 0.009577999822795 1.481505990028 3.649305820465 4.163704872131 -1.029266476631 1890 | 4.839981079102 7.906214237213 1.415740966797 18.49518966675 -15.86235141754 3.558807373047 1891 | -0.3502696752548 6.542941570282 0.07685226947069 12.89589309692 5.488034725189 5.854114055634 1892 | 0.4481022953987 6.554879665375 0.02459566108882 3.346600532532 1.348508954048 -2.017908573151 1893 | 0.2526816725731 6.646250247955 0.06909637898207 13.11480998993 5.120933532715 5.245120048523 1894 | 0.8494977355003 7.247109889984 0.07565788179636 3.348182439804 11.04000282288 -1.871989607811 1895 | -0.1806399971247 7.555788040161 0.09843933582306 8.100543022156 10.18719673157 -0.1975400000811 -------------------------------------------------------------------------------- /data/ETH_TLS/facade/s3_v0.1_sor.pcd: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ShiPC-AI/TCF/401a80a332a536f53722172baa658001d7d2fb0c/data/ETH_TLS/facade/s3_v0.1_sor.pcd -------------------------------------------------------------------------------- /data/KITTI/09/1380_v0.3.pcd: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ShiPC-AI/TCF/401a80a332a536f53722172baa658001d7d2fb0c/data/KITTI/09/1380_v0.3.pcd -------------------------------------------------------------------------------- /data/KITTI/09/1391-1380.pose: -------------------------------------------------------------------------------- 1 | 0.9999154497521 -0.009418637750745 -0.008962272777673 16.05701903367 2 | 0.009406629148241 0.9999547940405 -0.001378579370765 0.1046413725545 3 | 0.008974852175593 0.001294154612259 0.9999588414596 0.2728992351524 4 | 0 0 0 1 -------------------------------------------------------------------------------- /data/KITTI/09/1391_v0.3.pcd: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ShiPC-AI/TCF/401a80a332a536f53722172baa658001d7d2fb0c/data/KITTI/09/1391_v0.3.pcd -------------------------------------------------------------------------------- /data/KITTI/09/191_v0.3.pcd: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ShiPC-AI/TCF/401a80a332a536f53722172baa658001d7d2fb0c/data/KITTI/09/191_v0.3.pcd -------------------------------------------------------------------------------- /data/KITTI/09/210-191.pose: -------------------------------------------------------------------------------- 1 | 0.971502455353 0.2358215308277 0.02389765716511 16.01499599337 2 | -0.2359900832397 0.9717453047415 0.00445667809317 -2.181223620052 3 | -0.02217146218586 -0.009969283362499 0.9997044197254 -0.01418964364321 4 | 0 0 0 1 -------------------------------------------------------------------------------- /data/KITTI/09/210_v0.3.pcd: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ShiPC-AI/TCF/401a80a332a536f53722172baa658001d7d2fb0c/data/KITTI/09/210_v0.3.pcd -------------------------------------------------------------------------------- /data/KITTI/09/821_v0.3.pcd: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ShiPC-AI/TCF/401a80a332a536f53722172baa658001d7d2fb0c/data/KITTI/09/821_v0.3.pcd -------------------------------------------------------------------------------- /data/KITTI/09/833-821.pose: -------------------------------------------------------------------------------- 1 | 0.9992969203888 0.03694712827541 0.006365583233912 16.08298042792 2 | -0.03696606472637 0.9993124639907 0.002863189412543 -0.2466130644804 3 | -0.006255409455865 -0.003096481646896 0.9999756070476 0.2422946894812 4 | 0 0 0 1 -------------------------------------------------------------------------------- /data/KITTI/09/833_v0.3.pcd: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ShiPC-AI/TCF/401a80a332a536f53722172baa658001d7d2fb0c/data/KITTI/09/833_v0.3.pcd -------------------------------------------------------------------------------- /demo.cpp: -------------------------------------------------------------------------------- 1 | #include "registration/tcf.h" 2 | #include "utils/for_cloud.h" 3 | #include "utils/for_io.h" 4 | #include "utils/for_time.h" 5 | #include 6 | #include // for reading json file 7 | 8 | using json = nlohmann::json; 9 | int main(int argc, char** argv) { 10 | std::cout << "==========================================\n"; 11 | std::cout << "======== Demo of TCF Registration ========\n"; 12 | std::cout << "==========================================\n"; 13 | 14 | // Open the JSON file 15 | std::ifstream ifs("../config/config_eth.json"); // eth 16 | // std::ifstream ifs("../config/config_kitti.json"); // kitti 17 | if (!ifs.is_open()) { 18 | std::cout << "Cannot open config.json file.\n"; 19 | return 1; 20 | } 21 | 22 | // Parse the JSON file 23 | json config; 24 | ifs >> config; 25 | std::string path_source_cloud = config["path_source_cloud"]; 26 | std::string path_target_cloud = config["path_target_cloud"]; 27 | std::string path_matches = config["path_matches"]; 28 | std::string path_gt = config["path_gt"]; 29 | 30 | // Load ground-truth pose 31 | Eigen::Matrix4f gt = Eigen::Matrix4f::Identity(); 32 | loadMatrix44(path_gt, gt); 33 | std::cout << "GT pose: \n" << gt << "\n"; 34 | 35 | // Load point cloud and resolution 36 | // this can be replaced by a user-defined value 37 | CloudPtr source_cloud(new PointCloud), target_cloud(new PointCloud); 38 | pcl::io::loadPCDFile(path_source_cloud, *source_cloud); 39 | pcl::io::loadPCDFile(path_target_cloud, *target_cloud); 40 | float rs = pcResolution(source_cloud); 41 | float rt = pcResolution(target_cloud); 42 | float th = std::max(rs, rt); 43 | std::cout << "Resolution: " << th << " m\n"; 44 | 45 | // Load correspondences 46 | Eigen::MatrixXf matches; 47 | loadMatrixDynamic(path_matches, matches); 48 | MatfD3 source_match = matches.leftCols(3); 49 | MatfD3 target_match = matches.rightCols(3); 50 | 51 | // Start registration 52 | TicToc tic_tcf; 53 | std::srand(unsigned(std::time(nullptr))); 54 | Eigen::Matrix4f trans = twoStageConsensusFilter(source_match, target_match, 3*th); 55 | double time_registration = tic_tcf.toc(); 56 | std::cout << "Runtime: " << time_registration << " ms.\n"; 57 | 58 | // compute error 59 | std::pair error = computeTransError(trans, gt); 60 | std::cout << "RE: " << error.first << " deg, TE: " << error.second << " m.\n"; 61 | 62 | return 0; 63 | } -------------------------------------------------------------------------------- /figures/framework-a.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ShiPC-AI/TCF/401a80a332a536f53722172baa658001d7d2fb0c/figures/framework-a.png -------------------------------------------------------------------------------- /figures/framework-b.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ShiPC-AI/TCF/401a80a332a536f53722172baa658001d7d2fb0c/figures/framework-b.png -------------------------------------------------------------------------------- /registration/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | SET(LIB_NAME tcf_registration) 2 | SET(LIB_NAME_UPPER TCF_REGISTRATION) 3 | 4 | aux_source_directory(./ ${LIB_NAME_UPPER}_SRC) 5 | 6 | SET(${LIB_NAME_UPPER}_LIB ${LIB_NAME} CACHE INTERNAL "lib") 7 | 8 | ADD_LIBRARY(${${LIB_NAME_UPPER}_LIB} SHARED 9 | ${${LIB_NAME_UPPER}_SRC}) 10 | 11 | TARGET_LINK_LIBRARIES(${${LIB_NAME_UPPER}_LIB} 12 | ${PCL_LIBRARIES} ${TCF_UTILS_LIB}) -------------------------------------------------------------------------------- /registration/irls_welsch.cpp: -------------------------------------------------------------------------------- 1 | #include "registration/irls_welsch.h" 2 | 3 | Eigen::Matrix4f saCauchyIRLSRigidModel(const Matf3D& src, const Matf3D& dst, const float& tau) { 4 | float prev_cost = std::pow(10, 15); // initial energy cost 5 | int maxIter = 100; // maximum iteration times 6 | int n = src.cols(); // measurement num 7 | Eigen::MatrixXf weights = Eigen::MatrixXf::Ones(1, n); // weights vector 8 | float alpha = 0.0; 9 | 10 | Mati1D inlier_column = Eigen::VectorXi::LinSpaced(n, 0, n - 1).transpose(); // inlier col indices 11 | Eigen::Matrix4f trans = Eigen::Matrix4f::Identity(); // output pose 12 | 13 | Matf3D src_current = src; 14 | Matf3D dst_current = dst; 15 | for (int i = 1; i <= maxIter; ++i) { 16 | Matf3D src_temp = src_current(Eigen::all, inlier_column); 17 | Matf3D des_temp = dst_current(Eigen::all, inlier_column); 18 | src_current = src_temp; 19 | dst_current = des_temp; 20 | 21 | trans = rigidTrans(src_current, dst_current, weights); // compute transform 22 | Matf3D fit = (trans.block<3, 3>(0, 0) * src_current).colwise() + trans.block<3, 1>(0, 3); 23 | Matf1D residuals = (fit - dst_current).colwise().norm(); // compute error 24 | 25 | if (i == 1) { 26 | alpha = residuals.cwiseAbs().maxCoeff(); // intial alpha value 27 | } 28 | Matf1D residuals2 = residuals.array().square(); 29 | float cost = weights.cwiseProduct(residuals2).sum(); // current cost sum 30 | 31 | Mati1D flags = (residuals.array() < (3 * alpha)).cast(); 32 | inlier_column = getNonZeroColumnIndicesFromRowVector(flags); // update inlier indcies 33 | 34 | Matf1D E = residuals(Eigen::all, inlier_column); // update error of matching points 35 | weights = (E.array().square().array() + std::pow(alpha, 2)).cwiseInverse() * std::pow(alpha, 2); // update SA-Cauch Weights 36 | float cost_diff = std::abs(cost - prev_cost); // cost diff 37 | 38 | alpha = alpha / tau; // update alpha 39 | prev_cost = cost; // update cost 40 | if (cost_diff < 0.01 || alpha < 1.0) { 41 | break; 42 | } 43 | } 44 | 45 | return trans; 46 | } 47 | 48 | Eigen::Matrix4f rigidTrans(const Matf3D& A, const Matf3D& B, Eigen::MatrixXf& weights) { 49 | float sw = weights.sum(); 50 | if (sw < std::numeric_limits::epsilon()) { 51 | weights = Eigen::MatrixXf::Ones(1, A.cols()); 52 | sw = weights.sum(); 53 | } 54 | 55 | Eigen::MatrixXf w = weights / sw; 56 | Eigen::Matrix lc = A * w.transpose(); 57 | Eigen::Matrix rc = B * w.transpose(); 58 | Eigen::MatrixXf w2 = w.cwiseSqrt(); 59 | Eigen::MatrixXf w2_repmat(A.rows(), A.cols()); 60 | for (int i = 0; i < w2_repmat.rows(); ++i) { 61 | w2_repmat.row(i) = w2; 62 | } 63 | 64 | Eigen::MatrixXf left = (A.colwise() - lc).cwiseProduct(w2_repmat); 65 | Eigen::MatrixXf right = (B.colwise() - rc).cwiseProduct(w2_repmat); 66 | Eigen::MatrixXf M = left * right.transpose(); 67 | 68 | float Sxx = M(0,0); float Syx = M(1,0); float Szx = M(2,0); 69 | float Sxy = M(0,1); float Syy = M(1,1); float Szy = M(2,1); 70 | float Sxz = M(0,2); float Syz = M(1,2); float Szz = M(2,2); 71 | Eigen::Matrix4f N = Eigen::Matrix4f::Identity(); 72 | N << Sxx + Syy + Szz, Syz - Szy, Szx - Sxz, Sxy - Syx, 73 | Syz - Szy, Sxx - Syy - Szz, Sxy + Syx, Szx + Sxz, 74 | Szx - Sxz, Sxy + Syx, -Sxx + Syy - Szz, Syz + Szy, 75 | Sxy - Syx, Szx + Sxz, Syz + Szy, -Sxx - Syy + Szz; 76 | 77 | // eigen values and vectors 78 | Eigen::EigenSolver es(N); 79 | Eigen::Vector4f evalue = es.eigenvalues().real(); 80 | Eigen::Matrix4f evector = es.eigenvectors().real(); 81 | 82 | // max evalue and vector 83 | int maxRow = 0, maxCol = 0; 84 | evalue.maxCoeff(&maxRow, &maxCol); 85 | Eigen::Vector4f q = evector.col(maxRow); 86 | 87 | q.cwiseAbs().maxCoeff(&maxRow, &maxCol); 88 | float max_vec = q(maxRow, 0); 89 | if (max_vec < 0) { 90 | q = q * (-1); 91 | } 92 | q.normalize(); 93 | float q0 = q(0); 94 | float qx = q(1); 95 | float qy = q(2); 96 | float qz = q(3); 97 | Eigen::Vector3f v = q.tail(3); 98 | 99 | Eigen::Matrix3f Z = Eigen::Matrix3f::Identity(); 100 | Z << q0, -qz, qy, 101 | qz, q0, -qx, 102 | -qy, qx, q0; 103 | 104 | Eigen::Matrix3f R = v * v.transpose() + Z * Z; 105 | Eigen::Vector3f t = rc - R * lc; 106 | 107 | Eigen::Matrix4f T = Eigen::Matrix4f::Identity(); 108 | T.block<3, 3>(0, 0) = R; 109 | T.block<3, 1>(0, 3) = t; 110 | return T; 111 | } -------------------------------------------------------------------------------- /registration/irls_welsch.h: -------------------------------------------------------------------------------- 1 | #ifndef TCF_REGISTRATION_IRLS_WELSCH_H_ 2 | #define TCF_REGISTRATION_IRLS_WELSCH_H_ 3 | #include "utils/for_cloud.h" 4 | #include "registration/ransac_1pt2pt3pt.h" 5 | 6 | Eigen::Matrix4f saCauchyIRLSRigidModel(const Matf3D& src, const Matf3D& dst, const float& tau); 7 | Eigen::Matrix4f rigidTrans(const Matf3D& A, const Matf3D& B, Eigen::MatrixXf& weights); 8 | #endif -------------------------------------------------------------------------------- /registration/ransac_1pt2pt3pt.cpp: -------------------------------------------------------------------------------- 1 | #include "registration/ransac_1pt2pt3pt.h" 2 | 3 | Matf6D ransac1Pt(Matf6D& x, float t) { 4 | int s = 1; 5 | int max_trials = 10000; 6 | int npts = x.cols(); 7 | 8 | float p = 0.99; // Desired probability of choosing at least one samplefree from outliers 9 | int trialcount = 0; 10 | int bestscore = 0; 11 | Matf6D bestinliers; // 12 | 13 | float N = 1; // Dummy initialisation for number of trials. 14 | float t2 = 2.0 * t; // 15 | float eps = std::numeric_limits::epsilon(); 16 | 17 | while (N > trialcount) { 18 | int ind = std::rand() % npts; 19 | Eigen::Matrix seedpoint = x.col(ind); 20 | Matf6D lineset = x.colwise() - seedpoint; 21 | 22 | Matf1D D1 = lineset.topRows(3).colwise().norm(); 23 | Matf1D D2 = lineset.bottomRows(3).colwise().norm(); 24 | Matf1D len = (D1 - D2).array().abs(); 25 | 26 | Mati1D flag = (len.array() < t2).cast(); 27 | Mati1D inlier_column = getNonZeroColumnIndicesFromRowVector(flag); 28 | Matf6D inliers = x(Eigen::all, inlier_column); 29 | 30 | int s1 = inliers.cols(); 31 | int inlier_size = 0; // 32 | for (int i = 1; i <= 50; ++i) { 33 | Matf3D src = inliers.topRows(3); 34 | Matf3D dst = inliers.bottomRows(3); 35 | 36 | Eigen::MatrixXf src_dist_matrix(src.cols(), src.cols()); 37 | Eigen::MatrixXf dst_dist_matrix(dst.cols(), dst.cols()); 38 | computeDistanceMatrix(src, src_dist_matrix); 39 | computeDistanceMatrix(dst, dst_dist_matrix); 40 | Eigen::MatrixXf Z = (src_dist_matrix - dst_dist_matrix).array().abs(); 41 | Eigen::MatrixXi F = (Z.array() < t2).cast(); 42 | inlier_size = std::ceil(std::sqrt(F.sum())); 43 | 44 | Mati1D F_colwise_sum = F.colwise().sum(); 45 | std::vector sorted_column_indices_total; 46 | sortRowVectorDescending(F_colwise_sum, sorted_column_indices_total); 47 | 48 | std::vector sorted_column_indices_inlier(sorted_column_indices_total.begin(), 49 | sorted_column_indices_total.begin() + inlier_size); 50 | Matf6D selected_inliers = inliers(Eigen::all, sorted_column_indices_inlier); 51 | inliers = selected_inliers; 52 | 53 | if ((s1 - inlier_size) < 5) { 54 | break; 55 | } 56 | s1 = inlier_size; 57 | } 58 | 59 | if (inlier_size > bestscore) { 60 | bestscore = inlier_size; 61 | bestinliers = inliers; 62 | float fracinliers = static_cast(inlier_size) / npts; 63 | float pNoOutliers = 1 - std::pow(fracinliers, s); 64 | pNoOutliers = std::max(eps, pNoOutliers); // Avoid division by -Inf 65 | pNoOutliers = std::min(1 - eps, pNoOutliers); // Avoid division by 0. 66 | N = log(1-p)/log(pNoOutliers); 67 | N = std::max(N, static_cast(RansacN1)); // at least try 68 | } 69 | ++trialcount; 70 | 71 | if (trialcount > max_trials) { 72 | break; 73 | } 74 | } 75 | 76 | return bestinliers; 77 | } 78 | 79 | Mati1D getNonZeroColumnIndicesFromRowVector(const Mati1D& flags) { 80 | int count = 0; 81 | Mati1D nonzero_column(1, flags.count()); 82 | for (int i = 0; i < flags.cols(); ++i) { 83 | if (flags(0, i) > 0) { 84 | nonzero_column(0, count++) = i; 85 | } 86 | } 87 | return nonzero_column; 88 | } 89 | 90 | void computeDistanceMatrix(const Matf3D& data, Eigen::MatrixXf& dist_matrix) { 91 | for (int i = 0; i < data.cols(); ++i) 92 | dist_matrix.row(i) = (data.colwise() - data.col(i)).colwise().norm(); 93 | } 94 | 95 | void sortRowVectorDescending(const Mati1D& data, std::vector& sorted_column_indices) { 96 | std::vector sorted_data(data.cols()); 97 | sorted_column_indices.resize(data.cols()); 98 | for (int i = 0; i < data.cols(); ++i) { 99 | sorted_data[i] = data(0, i); 100 | sorted_column_indices[i] = i; 101 | } 102 | 103 | std::sort(sorted_column_indices.begin(), sorted_column_indices.end(), [&sorted_data](int i, int j) { 104 | return sorted_data[i] > sorted_data[j]; }); 105 | } 106 | 107 | 108 | Matf6D ransac2Pt(Matf6D& x, float t) { 109 | int s = 2; 110 | int max_trials = 10000; 111 | int npts = x.cols(); 112 | 113 | float p = 0.99; // Desired probability of choosing at least one samplefree from outliers 114 | int trialcount = 0; 115 | int bestscore = 0; 116 | Matf6D bestinliers; 117 | 118 | float N = 1; // Dummy initialisation for number of trials. 119 | float t2 = 2.0 * t; // 120 | float eps = std::numeric_limits::epsilon(); 121 | 122 | while (N > trialcount) { 123 | int id_1 = std::rand() % npts; 124 | int id_2 = std::rand() % npts; 125 | if (id_1 == id_2) { 126 | continue; 127 | } 128 | Eigen::Matrix diff = x.col(id_1) - x.col(id_2); 129 | float diff_dist = std::abs(diff.head(3).norm() - diff.tail(3).norm()); 130 | 131 | if (diff_dist > t2) { 132 | continue; 133 | } 134 | 135 | Matf6D xinliers = x; 136 | Matf6D lineset1 = x.colwise() - x.col(id_1); 137 | Matf6D lineset2 = x.colwise() - x.col(id_2); 138 | Matf1D len1 = (lineset1.topRows(3).colwise().norm() - lineset1.bottomRows(3).colwise().norm()).cwiseAbs(); 139 | Matf1D len2 = (lineset2.topRows(3).colwise().norm() - lineset2.bottomRows(3).colwise().norm()).cwiseAbs();; 140 | 141 | Mati1D flag1 = (len1.array() < t2).cast(); 142 | Mati1D flag2 = (len2.array() < t2).cast(); 143 | Mati1D flag = flag1.cwiseMin(flag2); 144 | Mati1D inlier_column_rough = getNonZeroColumnIndicesFromRowVector(flag); 145 | 146 | if (flag.sum() < 3) { 147 | continue; 148 | } 149 | 150 | Matf6D lineset1_f = lineset1(Eigen::all, inlier_column_rough); 151 | Matf6D lineset2_f = lineset2(Eigen::all, inlier_column_rough); 152 | Matf6D xinliers_temp = xinliers(Eigen::all, inlier_column_rough);// 153 | xinliers = xinliers_temp; 154 | Mati1D inlier_flag = Mati1D::Zero(1, lineset1_f.cols()); 155 | 156 | for (int i = 0; i < lineset1_f.cols(); ++i) { 157 | Eigen::Matrix xk = lineset1_f.col(i).head(3); 158 | Eigen::Matrix yk = lineset1_f.col(i).tail(3); 159 | Eigen::Matrix xl = lineset2_f.col(i).head(3); 160 | Eigen::Matrix yl = lineset2_f.col(i).tail(3); 161 | 162 | float ty = yk.dot(yl) / (yk.norm() * yl.norm() + 0.00001); 163 | if (ty < -1) { 164 | ty = -1; 165 | } else if (ty > 1) { 166 | ty = 1; 167 | } 168 | 169 | float tx = xk.dot(xl) / (xk.norm() * xl.norm() + 0.00001); 170 | if (tx < -1) { 171 | tx = -1; 172 | } else if (tx > 1) { 173 | tx = 1; 174 | } 175 | 176 | float thetay = std::acos(ty); 177 | float thetax = std::acos(tx); 178 | float alpha = std::asin(std::min(0.9999, 2 * t / (xk.norm() + 0.00001))); // 4 179 | float beta = std::asin(std::min(0.9999, 2 * t / (xl.norm() + 0.00001))); // 4 180 | float dtheta = std::abs(thetay - thetax); 181 | 182 | if (dtheta < alpha + beta) { 183 | inlier_flag(0, i) = 1; 184 | } 185 | } 186 | 187 | // Find the number of inliers to this model. 188 | Mati1D inlier_column_refined = getNonZeroColumnIndicesFromRowVector(inlier_flag); 189 | Matf6D updated_inliers = xinliers(Eigen::all, inlier_column_refined); 190 | Eigen::MatrixXf src_dist_matrix(updated_inliers.cols(), updated_inliers.cols()); 191 | Eigen::MatrixXf dst_dist_matrix(updated_inliers.cols(), updated_inliers.cols()); 192 | Matf3D src = updated_inliers.topRows(3); 193 | Matf3D dst = updated_inliers.bottomRows(3); 194 | computeDistanceMatrix(src, src_dist_matrix); 195 | computeDistanceMatrix(dst, dst_dist_matrix); 196 | 197 | Eigen::MatrixXi F = ((src_dist_matrix - dst_dist_matrix).array().abs() <= t2).cast(); 198 | int inlier_size = std::ceil(std::sqrt(F.sum())); 199 | 200 | if (inlier_size >= bestscore) { 201 | bestscore = inlier_size; 202 | bestinliers = xinliers(Eigen::all, inlier_column_refined); 203 | float fracinliers = (float)inlier_size / npts; 204 | float pNoOutliers = 1 - std::pow(fracinliers, s); 205 | pNoOutliers = std::max(eps, pNoOutliers); //Avoid division by -Inf 206 | pNoOutliers = std::min(1 - eps, pNoOutliers); //Avoid division by 0. 207 | N = log(1-p)/log(pNoOutliers); 208 | N = std::max(N, (float)RansacN2); // at least try RansacN2 times 209 | } 210 | ++trialcount; 211 | 212 | if (trialcount > max_trials) { 213 | break; 214 | } 215 | } 216 | return bestinliers; 217 | } 218 | 219 | ///////////////////////////////////////////////////////////// 220 | Eigen::Matrix4f ransac3Pt(const Matf6D& x, int s, float t) { 221 | int max_data_trials = 1000; 222 | int max_trials = 10000; 223 | int npts = x.cols(); 224 | 225 | float p = 0.99; //Desired probability of choosing at least one sample 226 | int trialcount = 0; 227 | int bestscore = 0; 228 | Mati1D bestinliers; // 229 | 230 | float N = 1; //Dummy initialisation for number of trials. 231 | float eps = std::numeric_limits::epsilon(); 232 | 233 | Eigen::Matrix4f trans = Eigen::Matrix4f::Identity(); 234 | Eigen::Matrix4f trans_best = Eigen::Matrix4f::Identity(); 235 | while (N > trialcount) { 236 | int count = 1; 237 | bool degenerate = true; 238 | 239 | while (degenerate) { 240 | int id_1 = std::rand() % npts; 241 | int id_2 = std::rand() % npts; 242 | int id_3 = std::rand() % npts; 243 | if (id_1 == id_2 || id_1 == id_3 || id_2 == id_3) { 244 | continue; 245 | } 246 | 247 | Eigen::Vector3i ind(id_1, id_2, id_3); 248 | degenerate = isDegenerate(x(Eigen::all, ind)); 249 | 250 | if (!degenerate) { 251 | trans = rigidMotion(x(Eigen::seq(0, 2), ind), x(Eigen::seq(3, 5), ind)); 252 | } 253 | 254 | ++count; 255 | if (count > max_data_trials) { 256 | break; 257 | } 258 | } 259 | 260 | Mati1D inlier_column = dist3d(trans, x, t); 261 | int inlier_size = inlier_column.cols(); 262 | 263 | if (inlier_size >= bestscore) { 264 | bestscore = inlier_size; 265 | bestinliers = inlier_column; 266 | trans_best = trans; 267 | 268 | float fracinliers = 0, pNoOutliers = 0; 269 | 270 | Mati1D loInliers = bestinliers; 271 | int loIter = 0; 272 | int loRansacMaxIter = 50;// 273 | int NOSample = std::min(s * 7, (int)loInliers.cols()); 274 | 275 | if (inlier_size < s) { 276 | fracinliers = static_cast(inlier_size) / npts; 277 | pNoOutliers = 1 - std::pow(fracinliers, s); 278 | pNoOutliers = std::max(eps, pNoOutliers); //Avoid division by -Inf 279 | pNoOutliers = std::min(1 - eps, pNoOutliers); //Avoid division by 0. 280 | N = log(1-p)/log(pNoOutliers); 281 | continue; 282 | } 283 | 284 | if (trialcount < 50) { 285 | loIter = loRansacMaxIter; 286 | } 287 | 288 | std::vector loInliers_vec(&loInliers(0, 0), loInliers.data() + loInliers.size()); 289 | while (loIter < loRansacMaxIter) { 290 | loIter = loIter + 1; 291 | std::random_shuffle(loInliers_vec.begin(), loInliers_vec.end()); 292 | std::vector loind(loInliers_vec.begin(), loInliers_vec.begin() + NOSample); 293 | trans = rigidMotion(x(Eigen::seq(0, 2), loind), x(Eigen::seq(3, 5), loind)); 294 | Mati1D loUpdatedInliers = dist3d(trans, x, t); 295 | 296 | if (loUpdatedInliers.cols() > bestscore) { 297 | bestscore = loUpdatedInliers.cols(); 298 | inlier_size = bestscore; 299 | bestinliers = loUpdatedInliers; 300 | trans_best = trans; 301 | } 302 | } 303 | 304 | fracinliers = inlier_size / static_cast(npts); 305 | pNoOutliers = 1.0 - std::pow(fracinliers, s); 306 | pNoOutliers = std::max(eps, pNoOutliers); 307 | pNoOutliers = std::min(1 - eps, pNoOutliers); 308 | N = log(1-p)/log(pNoOutliers); 309 | N = std::max(N, (float)RansacN3); // at least try 310 | } 311 | ++trialcount; 312 | 313 | if (trialcount > max_trials) { 314 | break; // Safeguard against being stuck in this loop forever 315 | } 316 | } 317 | 318 | if (trans_best.array().isNaN().any()) { 319 | std::cout << "Three-POint RANSAC's aatrix exist NaN.\n"; 320 | trans_best.setIdentity(); 321 | } 322 | return trans_best; 323 | } 324 | 325 | bool isDegenerate(const Eigen::Matrix& x) { 326 | Eigen::Matrix x1 = x.topRows(3); 327 | Eigen::Matrix x2 = x.bottomRows(3); 328 | bool flag_1 = iscolinear(x1.col(0), x1.col(1), x1.col(2)); 329 | bool flag_2 = iscolinear(x2.col(0), x2.col(1), x2.col(2)); 330 | if (flag_1 || flag_2) { 331 | return true; 332 | } else { 333 | return false; 334 | } 335 | } 336 | bool iscolinear(const Eigen::Vector3f& p1, const Eigen::Vector3f& p2, 337 | const Eigen::Vector3f& p3) { 338 | Eigen::Vector3f p1p2 = p2 - p1; 339 | Eigen::Vector3f p1p3 = p3 - p1; 340 | return (p1p2.cross(p1p3)).norm() < std::numeric_limits::epsilon(); 341 | } 342 | 343 | Eigen::Matrix4f rigidMotion(const Matf3D& A, const Matf3D& B) { 344 | Eigen::Matrix lc = A.rowwise().mean(); 345 | Eigen::Matrix rc = B.rowwise().mean(); 346 | Eigen::Matrix3f M = (A.colwise() - lc) * (B.colwise() - rc).transpose(); 347 | 348 | float Sxx = M(0,0); float Syx = M(1,0); float Szx = M(2,0); 349 | float Sxy = M(0,1); float Syy = M(1,1); float Szy = M(2,1); 350 | float Sxz = M(0,2); float Syz = M(1,2); float Szz = M(2,2); 351 | Eigen::Matrix4f N = Eigen::Matrix4f::Identity(); 352 | N << Sxx+Syy+Szz, Syz-Szy, Szx-Sxz, Sxy-Syx, 353 | Syz-Szy, Sxx-Syy-Szz, Sxy+Syx, Szx+Sxz, 354 | Szx-Sxz, Sxy+Syx, -Sxx+Syy-Szz, Syz+Szy, 355 | Sxy-Syx, Szx+Sxz, Syz+Szy, -Sxx-Syy+Szz; 356 | 357 | // eigen values and vectors 358 | Eigen::EigenSolver es(N); 359 | Eigen::Vector4f evalue = es.eigenvalues().real(); 360 | Eigen::Matrix4f evector = es.eigenvectors().real(); 361 | 362 | // max evalue and vector 363 | int max_row = 0, max_col = 0; 364 | evalue.maxCoeff(&max_row, &max_col); 365 | Eigen::Vector4f q = evector.col(max_row); 366 | 367 | q.cwiseAbs().maxCoeff(&max_row, &max_col); 368 | float max_vec = q(max_row, 0); 369 | if (max_vec < 0) { 370 | q = q * (-1); 371 | } 372 | q.normalize(); 373 | float q0 = q(0); 374 | float qx = q(1); 375 | float qy = q(2); 376 | float qz = q(3); 377 | Eigen::Vector3f v = q.tail(3); 378 | 379 | Eigen::Matrix3f Z = Eigen::Matrix3f::Identity(); 380 | Z << q0, -qz, qy, 381 | qz, q0, -qx, 382 | -qy, qx, q0; 383 | Eigen::Matrix3f R = v * v.transpose() + Z * Z; 384 | Eigen::Vector3f t = rc - R * lc; 385 | 386 | Eigen::Matrix4f T = Eigen::Matrix4f::Identity(); 387 | T.block<3, 3>(0, 0) = R; 388 | T.block<3, 1>(0, 3) = t; 389 | return T; 390 | } 391 | 392 | 393 | Mati1D dist3d(const Eigen::Matrix4f& trans, const Matf6D& x, const float t) { 394 | Eigen::Matrix3f R = trans.block<3, 3>(0, 0); 395 | Eigen::Vector3f T = trans.block<3, 1>(0, 3); 396 | Matf3D x2_ = (R * x.topRows(3)).colwise() + T; 397 | Matf1D d2 = (x2_ - x.bottomRows(3)).colwise().norm(); 398 | Mati1D flag = (d2.array() < t).cast(); 399 | return getNonZeroColumnIndicesFromRowVector(flag); 400 | } -------------------------------------------------------------------------------- /registration/ransac_1pt2pt3pt.h: -------------------------------------------------------------------------------- 1 | #ifndef TCF_REGISTRATION_RANSAC1PT2PT3PT_H_ 2 | #define TCF_REGISTRATION_RANSAC1PT2PT3PT_H_ 3 | #include "utils/for_cloud.h" 4 | 5 | // One-point RANSAC 6 | Matf6D ransac1Pt(Matf6D& x, float t); 7 | Mati1D getNonZeroColumnIndicesFromRowVector(const Mati1D& flags); 8 | void computeDistanceMatrix(const Matf3D& data, Eigen::MatrixXf& dist_matrix); 9 | void sortRowVectorDescending(const Mati1D& data, std::vector& sorted_column_indices); 10 | 11 | // Two-point RANSAC 12 | Matf6D ransac2Pt(Matf6D& x, float t); 13 | 14 | // Three-point RANSAC 15 | Eigen::Matrix4f ransac3Pt(const Matf6D& x, int s, float t); 16 | bool isDegenerate(const Eigen::Matrix& x); 17 | bool iscolinear(const Eigen::Matrix& p1, const Eigen::Matrix& p2, 18 | const Eigen::Matrix& p3); 19 | Eigen::Matrix4f rigidMotion(const Matf3D& A, const Matf3D& B); 20 | Mati1D dist3d(const Eigen::Matrix4f& trans, const Matf6D& x, const float t); 21 | #endif -------------------------------------------------------------------------------- /registration/tcf.cpp: -------------------------------------------------------------------------------- 1 | #include "registration/tcf.h" 2 | 3 | Eigen::Matrix4f twoStageConsensusFilter(MatfD3 match_1, MatfD3 match_2, float t) { 4 | Eigen::Matrix4f trans = Eigen::Matrix4f::Identity(); 5 | int source_num = match_1.rows(); 6 | int target_num = match_2.rows(); 7 | 8 | if (source_num != target_num) { 9 | PCL_ERROR("Correspondence must have the same dimension.\n"); 10 | return trans; 11 | } 12 | if (source_num < 3) { 13 | PCL_WARN("Must have at least 3 points to fitTING.\n"); 14 | return trans; 15 | } 16 | 17 | // two point cloud are 'stacked' to create a 6xN array for ransac 18 | Matf6D stacked_mat(6, source_num); 19 | stacked_mat.topRows(3) = match_1.transpose(); 20 | stacked_mat.bottomRows(3) = match_2.transpose(); 21 | 22 | // ONe-point RANSAC 23 | Matf6D xinliers_1 = ransac1Pt(stacked_mat, t); 24 | if (xinliers_1.cols() < 3) { 25 | PCL_WARN("ransac 1 matches less than 3\n"); 26 | return trans; 27 | } 28 | 29 | // Two-point RANSAC 30 | Matf6D xinliers_2 = ransac2Pt(xinliers_1, t); 31 | if (xinliers_2.cols() < 3) { 32 | PCL_WARN("ransac 2 matches less than 3\n"); 33 | return trans; 34 | } 35 | 36 | // Three-point RANSAC 37 | trans = ransac3Pt(xinliers_2, 3, t); 38 | Mati1D inlier_column = dist3d(trans, xinliers_2, t); 39 | Matf6D xinliers_3 = xinliers_2(Eigen::all, inlier_column); 40 | if (xinliers_3.cols() < 3) { 41 | PCL_WARN("ransac 3 matches less than 3\n"); 42 | return trans; 43 | } 44 | 45 | // SA-Cauchy IRLS; 46 | trans = saCauchyIRLSRigidModel(xinliers_3.topRows(3), xinliers_3.bottomRows(3), 1.3); 47 | return trans; 48 | } -------------------------------------------------------------------------------- /registration/tcf.h: -------------------------------------------------------------------------------- 1 | #ifndef TCF_REGISTRATION_TCF_H_ 2 | #define TCF_REGISTRATION_TCF_H_ 3 | #include "registration/ransac_1pt2pt3pt.h" 4 | #include "registration/irls_welsch.h" 5 | 6 | Eigen::Matrix4f twoStageConsensusFilter(MatfD3 match_1, MatfD3 match_2, float t); 7 | 8 | #endif 9 | -------------------------------------------------------------------------------- /utils/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | SET(LIB_NAME tcf_utils) 2 | SET(LIB_NAME_UPPER TCF_UTILS) 3 | 4 | aux_source_directory(./ ${LIB_NAME_UPPER}_SRC) 5 | 6 | SET(${LIB_NAME_UPPER}_LIB ${LIB_NAME} CACHE INTERNAL "lib") 7 | 8 | ADD_LIBRARY(${${LIB_NAME_UPPER}_LIB} SHARED 9 | ${${LIB_NAME_UPPER}_SRC}) 10 | 11 | TARGET_LINK_LIBRARIES(${${LIB_NAME_UPPER}_LIB} 12 | ${PCL_LIBRARIES}) -------------------------------------------------------------------------------- /utils/for_cloud.cpp: -------------------------------------------------------------------------------- 1 | #include "utils/for_cloud.h" 2 | 3 | void randomSampleCloud(const CloudPtr& cloud_in, 4 | CloudPtr& cloud_out, int N) { 5 | pcl::RandomSample random_sampler; 6 | random_sampler.setInputCloud(cloud_in); 7 | random_sampler.setSample(N); 8 | random_sampler.filter(*cloud_out); 9 | } 10 | 11 | void voxelSampleCloud(const CloudPtr& cloud_in, CloudPtr& cloud_out, const float leaf) { 12 | pcl::VoxelGrid grid; 13 | grid.setInputCloud(cloud_in); 14 | grid.setLeafSize(leaf, leaf, leaf); 15 | grid.filter(*cloud_out); 16 | } 17 | 18 | float pcResolution(CloudPtr& cloud) { 19 | pcl::search::KdTree::Ptr tree (new pcl::search::KdTree); 20 | tree->setInputCloud(cloud); 21 | 22 | // sample size 23 | int search_num = std::floor((int)cloud->size() / 20) * 10; 24 | int pt_num = cloud->size(); 25 | 26 | std::vector distances(search_num); 27 | for (int i = 0; i < search_num; ++i) { 28 | int idx = std::rand() % pt_num; 29 | std::vector idx_nknsearch; 30 | std::vector sqdist_nknsearch; 31 | tree->nearestKSearch(cloud->points[idx], 2, idx_nknsearch, sqdist_nknsearch); 32 | distances[i] = sqdist_nknsearch[1]; 33 | } 34 | std::sort(distances.begin(), distances.end()); 35 | int id_mid = (int)(search_num - 1) / 2; 36 | return std::sqrt(distances[id_mid]); 37 | } 38 | 39 | void matrixN3toCloud(const MatfD3& mat, CloudPtr& cloud) { 40 | cloud->resize(mat.rows()); 41 | for (int i = 0; i < mat.rows(); ++i) { 42 | cloud->points[i].x = mat(i, 0); 43 | cloud->points[i].y = mat(i, 1); 44 | cloud->points[i].z = mat(i, 2); 45 | std::cout << cloud->points[i].getVector3fMap().transpose() << "\n"; 46 | } 47 | } 48 | 49 | std::pair computeTransError(const Eigen::Matrix4f& trans, const Eigen::Matrix4f& gt) { 50 | Eigen::Matrix3f eR = gt.block<3, 3>(0, 0) * trans.block<3, 3>(0, 0).transpose(); 51 | double er_cos = std::max(-1.0, std::min(1.0, (eR.trace() - 1.0)/2.0)); 52 | double er_degree = std::acos(er_cos) * 180.0 / M_PI; 53 | double et = (trans - gt).block<3, 1>(0, 3).norm(); 54 | return std::make_pair(er_degree, et); 55 | } -------------------------------------------------------------------------------- /utils/for_cloud.h: -------------------------------------------------------------------------------- 1 | #ifndef TCF_UTILS_CLOUD_H_ 2 | #define TCF_UTILS_CLOUD_H_ 3 | 4 | #include 5 | #include 6 | #include 7 | #include 8 | #include 9 | #include 10 | #include 11 | 12 | // ransac 13 | #define RansacN1 0 14 | #define RansacN2 0 15 | #define RansacN3 0 16 | 17 | // point cloud 18 | typedef pcl::PointCloud PointCloud; 19 | typedef pcl::PointCloud::Ptr CloudPtr; 20 | 21 | // eigen matrix 22 | // Mat, D:dynamic 23 | typedef Eigen::Matrix Matf6D; 24 | typedef Eigen::Matrix Matf3D; 25 | typedef Eigen::Matrix MatfD3; 26 | typedef Eigen::Matrix Mati1D; 27 | typedef Eigen::Matrix Matf1D; 28 | 29 | // Random downsample the point cloud 30 | void randomSampleCloud(const CloudPtr& cloud_in, CloudPtr& cloud_out, int N); 31 | 32 | // Voxel downsample the point cloud 33 | void voxelSampleCloud(const CloudPtr& cloud_in, CloudPtr& cloud_out, const float leaf); 34 | 35 | // Estimate the resolution of the point cloud 36 | float pcResolution(CloudPtr& cloud); 37 | 38 | // Dynamic float N*3-type matrix into point cloud 39 | void matrixN3toCloud(const MatfD3& mat, CloudPtr& cloud); 40 | 41 | // Estimate registraiton error 42 | std::pair computeTransError(const Eigen::Matrix4f& trans, const Eigen::Matrix4f& gt); 43 | 44 | #endif 45 | -------------------------------------------------------------------------------- /utils/for_io.h: -------------------------------------------------------------------------------- 1 | #ifndef TCF_UTILS_IO_H_ 2 | #define TCF_UTILS_IO_H_ 3 | 4 | #include // for setprecision 5 | #include // for EXIT_FAILURE 6 | #include 7 | #include 8 | #include 9 | #include 10 | #include 11 | #include 12 | 13 | template 14 | void saveMatrixDynamic(const std::string& filename, Eigen::Matrix& mat, const int& N) { 15 | std::ofstream ofs(filename); 16 | if (!ofs.is_open()) 17 | std::exit((std::cout << "Failed to save:" << filename << "\n", EXIT_FAILURE)); 18 | ofs << std::setprecision(N); 19 | for (int i = 0; i < mat.rows(); ++i) { 20 | for (int j = 0; j < mat.cols(); ++j) 21 | ofs << mat(i, j) << " "; 22 | ofs << "\n"; 23 | } 24 | ofs.close(); 25 | } 26 | 27 | template 28 | void loadMatrixDynamic(const std::string& filename, Eigen::Matrix& mat) { 29 | std::ifstream ifs(filename); 30 | if (!ifs.is_open()) 31 | std::exit((std::cout << "Failed to open:" << filename << "\n", EXIT_FAILURE)); 32 | 33 | std::vector> values; 34 | std::string line; 35 | while (std::getline(ifs, line)) { 36 | std::istringstream ss(line); 37 | values.emplace_back(std::istream_iterator{ss}, std::istream_iterator{}); // C++ >=17 38 | } 39 | 40 | if (values.empty() || values[0].empty()) 41 | std::exit((std::cout << "Matrix data is empty or malformed.", EXIT_FAILURE)); 42 | mat.resize(values.size(), values[0].size()); 43 | for (int i = 0; i < values.size(); ++i) 44 | for (int j = 0; j < values[0].size(); ++j) 45 | mat(i, j) = values[i][j]; 46 | ifs.close(); 47 | } 48 | 49 | template 50 | void loadMatrix44(const std::string& filename, Eigen::Matrix& matrix) { 51 | matrix.setIdentity(); 52 | std::ifstream ifs(filename); 53 | if (!ifs.is_open()) 54 | std::exit((std::cout << "Failed to open 4*4 matrix file:" << filename << "\n", EXIT_FAILURE)); 55 | 56 | ifs >> matrix(0, 0) >> matrix(0, 1) >> matrix(0, 2) >> matrix(0, 3) 57 | >> matrix(1, 0) >> matrix(1, 1) >> matrix(1, 2) >> matrix(1, 3) 58 | >> matrix(2, 0) >> matrix(2, 1) >> matrix(2, 2) >> matrix(2, 3) 59 | >> matrix(3, 0) >> matrix(3, 1) >> matrix(3, 2) >> matrix(3, 3); 60 | ifs.close(); 61 | }; 62 | 63 | template 64 | void saveMatrix44(const std::string& filename, const Eigen::Matrix& matrix, const int& N) { 65 | std::ofstream ofs(filename); 66 | if (!ofs.is_open()) 67 | std::exit((std::cout << "Failed to save 4*4 matrix file:" << filename << "\n", EXIT_FAILURE)); 68 | ofs << std::setprecision(N); 69 | ofs << double(matrix(0, 0)) << " " << double(matrix(0, 1)) << " " << double(matrix(0, 2)) << " " << double(matrix(0, 3)) << std::endl 70 | << double(matrix(1, 0)) << " " << double(matrix(1, 1)) << " " << double(matrix(1, 2)) << " " << double(matrix(1, 3)) << std::endl 71 | << double(matrix(2, 0)) << " " << double(matrix(2, 1)) << " " << double(matrix(2, 2)) << " " << double(matrix(2, 3)) << std::endl 72 | << double(matrix(3, 0)) << " " << double(matrix(3, 1)) << " " << double(matrix(3, 2)) << " " << double(matrix(3, 3)); 73 | ofs.close(); 74 | }; 75 | #endif -------------------------------------------------------------------------------- /utils/for_misc.h: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ShiPC-AI/TCF/401a80a332a536f53722172baa658001d7d2fb0c/utils/for_misc.h -------------------------------------------------------------------------------- /utils/for_time.h: -------------------------------------------------------------------------------- 1 | #ifndef TCF_UTILS_TIME_H_ 2 | #define TCF_UTILS_TIME_H_ 3 | 4 | #include 5 | // time counter 6 | class TicToc { 7 | public: 8 | TicToc() { 9 | tic(); 10 | } 11 | void tic() { 12 | start = std::chrono::system_clock::now(); 13 | } 14 | 15 | double toc(){ 16 | end = std::chrono::system_clock::now(); 17 | std::chrono::duration elapsed_seconds = end - start; 18 | return elapsed_seconds.count() * 1000; 19 | } 20 | private: 21 | std::chrono::time_point start, end; 22 | }; 23 | 24 | #endif 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