PCL 基于对应分组的3D物体识别
本教程旨在说明如何基于pcl_recognition模块执行 3D 对象识别。具体而言，它解释了如何使用对应分组算法，以便将 3D 描述符匹配阶段之后获得的点对点对应关系集聚类到当前场景中存在的模型实例中。对于每个表示场景中可能的模型实例的聚类，对应分组算法还会输出转换矩阵，用于标识该模型在当前场景中的 6DOF 姿态估计。
#include <pcl/io/pcd_io.h>
#include <pcl/point_cloud.h>
#include <pcl/correspondence.h>
#include <pcl/features/normal_3d_omp.h>
#include <pcl/features/shot_omp.h>
#include <pcl/features/board.h>
#include <pcl/filters/uniform_sampling.h>
#include <pcl/recognition/cg/hough_3d.h>
#include <pcl/recognition/cg/geometric_consistency.h>
#include <pcl/visualization/pcl_visualizer.h>
#include <pcl/kdtree/kdtree_flann.h>
#include <pcl/kdtree/impl/kdtree_flann.hpp>
#include <pcl/common/transforms.h>
#include <pcl/console/parse.h>
typedef pcl::PointXYZRGBA PointType;
typedef pcl::Normal NormalType;
typedef pcl::ReferenceFrame RFType;
typedef pcl::SHOT352 DescriptorType;
std::string model_filename_;
std::string scene_filename_;
//Algorithm params
bool show_keypoints_(false);
bool show_correspondences_(false);
bool use_cloud_resolution_(false);
bool use_hough_(true);
float model_ss_(0.01f);
float scene_ss_(0.03f);
float rf_rad_(0.015f);
float descr_rad_(0.02f);
float cg_size_(0.01f);
float cg_thresh_(5.0f);
void
showHelp(char* filename)
{
std::cout << std::endl;
std::cout << "***************************************************************************" << std::endl;
std::cout << "* *" << std::endl;
std::cout << "* Correspondence Grouping Tutorial - Usage Guide *" << std::endl;
std::cout << "* *" << std::endl;
std::cout << "***************************************************************************" << std::endl << std::endl;
std::cout << "Usage: " << filename << " model_filename.pcd scene_filename.pcd [Options]" << std::endl << std::endl;
std::cout << "Options:" << std::endl;
std::cout << " -h: Show this help." << std::endl;
std::cout << " -k: Show used keypoints." << std::endl;
std::cout << " -c: Show used correspondences." << std::endl;
std::cout << " -r: Compute the model cloud resolution and multiply" << std::endl;
std::cout << " each radius given by that value." << std::endl;
std::cout << " --algorithm (Hough|GC): Clustering algorithm used (default Hough)." << std::endl;
std::cout << " --model_ss val: Model uniform sampling radius (default 0.01)" << std::endl;
std::cout << " --scene_ss val: Scene uniform sampling radius (default 0.03)" << std::endl;
std::cout << " --rf_rad val: Reference frame radius (default 0.015)" << std::endl;
std::cout << " --descr_rad val: Descriptor radius (default 0.02)" << std::endl;
std::cout << " --cg_size val: Cluster size (default 0.01)" << std::endl;
std::cout << " --cg_thresh val: Clustering threshold (default 5)" << std::endl << std::endl;
}
void
parseCommandLine(int argc, char* argv[])
{
//Show help
if (pcl::console::find_switch(argc, argv, "-h"))
{
showHelp(argv[0]);
exit(0);
}
//Model & scene filenames
std::vector<int> filenames;
filenames = pcl::console::parse_file_extension_argument(argc, argv, ".pcd");
if (filenames.size() != 2)
{
std::cout << "Filenames missing.\n";
showHelp(argv[0]);
exit(-1);
}
model_filename_ = argv[filenames[0]];
scene_filename_ = argv[filenames[1]];
//Program behavior
if (pcl::console::find_switch(argc, argv, "-k"))
{
show_keypoints_ = true;
}
if (pcl::console::find_switch(argc, argv, "-c"))
{
show_correspondences_ = true;
}
if (pcl::console::find_switch(argc, argv, "-r"))
{
use_cloud_resolution_ = true;
}
std::string used_algorithm;
if (pcl::console::parse_argument(argc, argv, "--algorithm", used_algorithm) != -1)
{
if (used_algorithm.compare("Hough") == 0)
{
use_hough_ = true;
}
else if (used_algorithm.compare("GC") == 0)
{
use_hough_ = false;
}
else
{
std::cout << "Wrong algorithm name.\n";
showHelp(argv[0]);
exit(-1);
}
}
//General parameters
pcl::console::parse_argument(argc, argv, "--model_ss", model_ss_);
pcl::console::parse_argument(argc, argv, "--scene_ss", scene_ss_);
pcl::console::parse_argument(argc, argv, "--rf_rad", rf_rad_);
pcl::console::parse_argument(argc, argv, "--descr_rad", descr_rad_);
pcl::console::parse_argument(argc, argv, "--cg_size", cg_size_);
pcl::console::parse_argument(argc, argv, "--cg_thresh", cg_thresh_);
}
double
computeCloudResolution(const pcl::PointCloud<PointType>::ConstPtr& cloud)
{
double res = 0.0;
int n_points = 0;
int nres;
std::vector<int> indices(2);
std::vector<float> sqr_distances(2);
pcl::search::KdTree<PointType> tree;
tree.setInputCloud(cloud);
for (std::size_t i = 0; i < cloud->size(); ++i)
{
if (!std::isfinite((*cloud)[i].x))
{
continue;
}
//Considering the second neighbor since the first is the point itself.
nres = tree.nearestKSearch(i, 2, indices, sqr_distances);
if (nres == 2)
{
res += sqrt(sqr_distances[1]);
++n_points;
}
}
if (n_points != 0)
{
res /= n_points;
}
return res;
}
int
main(int argc, char* argv[])
{
parseCommandLine(argc, argv);
// correspondence_grouping milk.pcd milk_cartoon_all_small_clorox.pcd
// parseCommandLine(correspondence_grouping, milk.pcd milk_cartoon_all_small_clorox.pcd);
pcl::PointCloud<PointType>::Ptr model(new pcl::PointCloud<PointType>());
pcl::PointCloud<PointType>::Ptr model_keypoints(new pcl::PointCloud<PointType>());
pcl::PointCloud<PointType>::Ptr scene(new pcl::PointCloud<PointType>());
pcl::PointCloud<PointType>::Ptr scene_keypoints(new pcl::PointCloud<PointType>());
pcl::PointCloud<NormalType>::Ptr model_normals(new pcl::PointCloud<NormalType>());
pcl::PointCloud<NormalType>::Ptr scene_normals(new pcl::PointCloud<NormalType>());
pcl::PointCloud<DescriptorType>::Ptr model_descriptors(new pcl::PointCloud<DescriptorType>());
pcl::PointCloud<DescriptorType>::Ptr scene_descriptors(new pcl::PointCloud<DescriptorType>());
//
// Load clouds
//
if (pcl::io::loadPCDFile(model_filename_, *model) < 0)
{
std::cout << "Error loading model cloud." << std::endl;
showHelp(argv[0]);
return (-1);
}
if (pcl::io::loadPCDFile(scene_filename_, *scene) < 0)
{
std::cout << "Error loading scene cloud." << std::endl;
showHelp(argv[0]);
return (-1);
}
//
// Set up resolution invariance
//
if (use_cloud_resolution_)
{
float resolution = static_cast<float> (computeCloudResolution(model));
if (resolution != 0.0f)
{
model_ss_ *= resolution;
scene_ss_ *= resolution;
rf_rad_ *= resolution;
descr_rad_ *= resolution;
cg_size_ *= resolution;
}
std::cout << "Model resolution: " << resolution << std::endl;
std::cout << "Model sampling size: " << model_ss_ << std::endl;
std::cout << "Scene sampling size: " << scene_ss_ << std::endl;
std::cout << "LRF support radius: " << rf_rad_ << std::endl;
std::cout << "SHOT descriptor radius: " << descr_rad_ << std::endl;
std::cout << "Clustering bin size: " << cg_size_ << std::endl << std::endl;
}
//
// Compute Normals
//
pcl::NormalEstimationOMP<PointType, NormalType> norm_est;
norm_est.setKSearch(10);
norm_est.setInputCloud(model);
norm_est.compute(*model_normals);
norm_est.setInputCloud(scene);
norm_est.compute(*scene_normals);
//
// Downsample Clouds to Extract keypoints
//
pcl::UniformSampling<PointType> uniform_sampling;
uniform_sampling.setInputCloud(model);
uniform_sampling.setRadiusSearch(model_ss_);
uniform_sampling.filter(*model_keypoints);
std::cout << "Model total points: " << model->size() << "; Selected Keypoints: " << model_keypoints->size() << std::endl;
uniform_sampling.setInputCloud(scene);
uniform_sampling.setRadiusSearch(scene_ss_);
uniform_sampling.filter(*scene_keypoints);
std::cout << "Scene total points: " << scene->size() << "; Selected Keypoints: " << scene_keypoints->size() << std::endl;
//
// Compute Descriptor for keypoints
//
pcl::SHOTEstimationOMP<PointType, NormalType, DescriptorType> descr_est;
descr_est.setRadiusSearch(descr_rad_);
descr_est.setInputCloud(model_keypoints);
descr_est.setInputNormals(model_normals);
descr_est.setSearchSurface(model);
descr_est.compute(*model_descriptors);
descr_est.setInputCloud(scene_keypoints);
descr_est.setInputNormals(scene_normals);
descr_est.setSearchSurface(scene);
descr_est.compute(*scene_descriptors);
//
// Find Model-Scene Correspondences with KdTree
//
pcl::CorrespondencesPtr model_scene_corrs(new pcl::Correspondences());
pcl::KdTreeFLANN<DescriptorType> match_search;
match_search.setInputCloud(model_descriptors);
// For each scene keypoint descriptor, find nearest neighbor into the model keypoints descriptor cloud and add it to the correspondences vector.
for (std::size_t i = 0; i < scene_descriptors->size(); ++i)
{
std::vector<int> neigh_indices(1);
std::vector<float> neigh_sqr_dists(1);
if (!std::isfinite(scene_descriptors->at(i).descriptor[0])) //skipping NaNs
{
continue;
}
int found_neighs = match_search.nearestKSearch(scene_descriptors->at(i), 1, neigh_indices, neigh_sqr_dists);
if (found_neighs == 1 && neigh_sqr_dists[0] < 0.25f) // add match only if the squared descriptor distance is less than 0.25 (SHOT descriptor distances are between 0 and 1 by design)
{
pcl::Correspondence corr(neigh_indices[0], static_cast<int> (i), neigh_sqr_dists[0]);
model_scene_corrs->push_back(corr);
}
}
std::cout << "Correspondences found: " << model_scene_corrs->size() << std::endl;
//
// Actual Clustering
//
std::vector<Eigen::Matrix4f, Eigen::aligned_allocator<Eigen::Matrix4f> > rototranslations;
std::vector<pcl::Correspondences> clustered_corrs;
// Using Hough3D
if (use_hough_)
{
//
// Compute (Keypoints) Reference Frames only for Hough
//
pcl::PointCloud<RFType>::Ptr model_rf(new pcl::PointCloud<RFType>());
pcl::PointCloud<RFType>::Ptr scene_rf(new pcl::PointCloud<RFType>());
pcl::BOARDLocalReferenceFrameEstimation<PointType, NormalType, RFType> rf_est;
rf_est.setFindHoles(true);
rf_est.setRadiusSearch(rf_rad_);
rf_est.setInputCloud(model_keypoints);
rf_est.setInputNormals(model_normals);
rf_est.setSearchSurface(model);
rf_est.compute(*model_rf);
rf_est.setInputCloud(scene_keypoints);
rf_est.setInputNormals(scene_normals);
rf_est.setSearchSurface(scene);
rf_est.compute(*scene_rf);
// Clustering
pcl::Hough3DGrouping<PointType, PointType, RFType, RFType> clusterer;
clusterer.setHoughBinSize(cg_size_);
clusterer.setHoughThreshold(cg_thresh_);
clusterer.setUseInterpolation(true);
clusterer.setUseDistanceWeight(false);
clusterer.setInputCloud(model_keypoints);
clusterer.setInputRf(model_rf);
clusterer.setSceneCloud(scene_keypoints);
clusterer.setSceneRf(scene_rf);
clusterer.setModelSceneCorrespondences(model_scene_corrs);
//clusterer.cluster (clustered_corrs);
clusterer.recognize(rototranslations, clustered_corrs);
}
else // Using GeometricConsistency
{
pcl::GeometricConsistencyGrouping<PointType, PointType> gc_clusterer;
gc_clusterer.setGCSize(cg_size_);
gc_clusterer.setGCThreshold(cg_thresh_);
gc_clusterer.setInputCloud(model_keypoints);
gc_clusterer.setSceneCloud(scene_keypoints);
gc_clusterer.setModelSceneCorrespondences(model_scene_corrs);
//gc_clusterer.cluster (clustered_corrs);
gc_clusterer.recognize(rototranslations, clustered_corrs);
}
//
// Output results
//
std::cout << "Model instances found: " << rototranslations.size() << std::endl;
for (std::size_t i = 0; i < rototranslations.size(); ++i)
{
std::cout << "\n Instance " << i + 1 << ":" << std::endl;
std::cout << " Correspondences belonging to this instance: " << clustered_corrs[i].size() << std::endl;
// Print the rotation matrix and translation vector
Eigen::Matrix3f rotation = rototranslations[i].block<3, 3>(0, 0);
Eigen::Vector3f translation = rototranslations[i].block<3, 1>(0, 3);
printf("\n");
printf(" | %6.3f %6.3f %6.3f | \n", rotation(0, 0), rotation(0, 1), rotation(0, 2));
printf(" R = | %6.3f %6.3f %6.3f | \n", rotation(1, 0), rotation(1, 1), rotation(1, 2));
printf(" | %6.3f %6.3f %6.3f | \n", rotation(2, 0), rotation(2, 1), rotation(2, 2));
printf("\n");
printf(" t = < %0.3f, %0.3f, %0.3f >\n", translation(0), translation(1), translation(2));
}
//
// Visualization
//
pcl::visualization::PCLVisualizer viewer("Correspondence Grouping");
viewer.addPointCloud(scene, "scene_cloud");
pcl::PointCloud<PointType>::Ptr off_scene_model(new pcl::PointCloud<PointType>());
pcl::PointCloud<PointType>::Ptr off_scene_model_keypoints(new pcl::PointCloud<PointType>());
if (show_correspondences_ || show_keypoints_)
{
// We are translating the model so that it doesn't end in the middle of the scene representation
pcl::transformPointCloud(*model, *off_scene_model, Eigen::Vector3f(-1, 0, 0), Eigen::Quaternionf(1, 0, 0, 0));
pcl::transformPointCloud(*model_keypoints, *off_scene_model_keypoints, Eigen::Vector3f(-1, 0, 0), Eigen::Quaternionf(1, 0, 0, 0));
pcl::visualization::PointCloudColorHandlerCustom<PointType> off_scene_model_color_handler(off_scene_model, 255, 255, 128);
viewer.addPointCloud(off_scene_model, off_scene_model_color_handler, "off_scene_model");
}
if (show_keypoints_)
{
pcl::visualization::PointCloudColorHandlerCustom<PointType> scene_keypoints_color_handler(scene_keypoints, 0, 0, 255);
viewer.addPointCloud(scene_keypoints, scene_keypoints_color_handler, "scene_keypoints");
viewer.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 5, "scene_keypoints");
pcl::visualization::PointCloudColorHandlerCustom<PointType> off_scene_model_keypoints_color_handler(off_scene_model_keypoints, 0, 0, 255);
viewer.addPointCloud(off_scene_model_keypoints, off_scene_model_keypoints_color_handler, "off_scene_model_keypoints");
viewer.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 5, "off_scene_model_keypoints");
}
for (std::size_t i = 0; i < rototranslations.size(); ++i)
{
pcl::PointCloud<PointType>::Ptr rotated_model(new pcl::PointCloud<PointType>());
pcl::transformPointCloud(*model, *rotated_model, rototranslations[i]);
std::stringstream ss_cloud;
ss_cloud << "instance" << i;
pcl::visualization::PointCloudColorHandlerCustom<PointType> rotated_model_color_handler(rotated_model, 255, 0, 0);
viewer.addPointCloud(rotated_model, rotated_model_color_handler, ss_cloud.str());
if (show_correspondences_)
{
for (std::size_t j = 0; j < clustered_corrs[i].size(); ++j)
{
std::stringstream ss_line;
ss_line << "correspondence_line" << i << "_" << j;
PointType& model_point = off_scene_model_keypoints->at(clustered_corrs[i][j].index_query);
PointType& scene_point = scene_keypoints->at(clustered_corrs[i][j].index_match);
// We are drawing a line for each pair of clustered correspondences found between the model and the scene
viewer.addLine<PointType, PointType>(model_point, scene_point, 0, 255, 0, ss_line.str());
}
}
}
while (!viewer.wasStopped())
{
viewer.spinOnce();
}
return (0);
}
使用VS调试时，在项目—属性—配置属性—调试—命令参数填写指令
milk.pcd milk_cartoon_all_small_clorox.pcd -k -c
请记住按此确切顺序将 和 替换为模型和场景文件名。如果需要，可以添加其他命令行选项，显示不同的效果，具体指令见提示：
std::cout << std::endl;
std::cout << "***************************************************************************" << std::endl;
std::cout << "* *" << std::endl;
std::cout << "* Correspondence Grouping Tutorial - Usage Guide *" << std::endl;
std::cout << "* *" << std::endl;
std::cout << "***************************************************************************" << std::endl << std::endl;
std::cout << "Usage: " << filename << " model_filename.pcd scene_filename.pcd [Options]" << std::endl << std::endl;
std::cout << "Options:" << std::endl;
std::cout << " -h: Show this help." << std::endl;
std::cout << " -k: Show used keypoints." << std::endl;
std::cout << " -c: Show used correspondences." << std::endl;
std::cout << " -r: Compute the model cloud resolution and multiply" << std::endl;
std::cout << " each radius given by that value." << std::endl;
std::cout << " --algorithm (Hough|GC): Clustering algorithm used (default Hough)." << std::endl;
std::cout << " --model_ss val: Model uniform sampling radius (default 0.01)" << std::endl;
std::cout << " --scene_ss val: Scene uniform sampling radius (default 0.03)" << std::endl;
std::cout << " --rf_rad val: Reference frame radius (default 0.015)" << std::endl;
std::cout << " --descr_rad val: Descriptor radius (default 0.02)" << std::endl;
std::cout << " --cg_size val: Cluster size (default 0.01)" << std::endl;
std::cout << " --cg_thresh val: Clustering threshold (default 5)" << std::endl << std::endl;
点云文件链接：https://download.csdn.net/download/baidu_41706898/86522095?spm=1001.2014.3001.5503
参考链接：https://pcl.readthedocs.io/projects/tutorials/en/latest/correspondence_grouping.html#correspondence-grouping