Code Description:
1. The model point cloud, the cloud point of the scene at each sampling, to obtain sparse key;
2. The model point cloud, the scene point cloud key point descriptors are calculated;
3. Use KdTreeFLANN corresponding point is searched for;
[4.] The clustering algorithm corresponding point [] corresponding point of the cluster to be recognized as a model;
5. Return to identify a transformation matrix for each model (translation + rotation matrix matrix), and the corresponding point on the clustering result
#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/keypoints/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"))//可视化构造对应点时用到的关键点
{
show_keypoints_ = true;
}
if (pcl::console::find_switch (argc, argv, "-c"))//可视化支持实例假设的对应点对
{
show_correspondences_ = true;
}
if (pcl::console::find_switch (argc, argv, "-r"))//计算点云的分辨率和多样性
{
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 (size_t i = 0; i < cloud->size (); ++i)
{
if (! pcl_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);
pcl::PointCloud<PointType>::Ptr model (new pcl::PointCloud<PointType> ()); //模型点云
pcl::PointCloud<PointType>::Ptr model_keypoints (new pcl::PointCloud<PointType> ()); //模型角点
pcl::PointCloud<PointType>::Ptr scene (new pcl::PointCloud<PointType> ()); //目标点云
pcl::PointCloud<PointType>::Ptr scene_keypoints (new pcl::PointCloud<PointType> ()); //目标角点
pcl::PointCloud<NormalType>::Ptr model_normals (new pcl::PointCloud<NormalType> ()); //法线
pcl::PointCloud<NormalType>::Ptr scene_normals (new pcl::PointCloud<NormalType> ()); //
pcl::PointCloud<DescriptorType>::Ptr model_descriptors (new pcl::PointCloud<DescriptorType> ()); //描述子
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：计算法线
//
pcl::NormalEstimationOMP<PointType, NormalType> norm_est;
norm_est.setNumberOfThreads(4); //手动设置线程数
norm_est.setKSearch (10); //设置k邻域搜索阈值为10个点
norm_est.setInputCloud (model); //设置输入模型点云
norm_est.compute (*model_normals);//计算点云法线
norm_est.setInputCloud (scene);
norm_est.compute (*scene_normals);
//
// Downsample Clouds to Extract keypoints：均匀采样点云并提取关键点
// 类UniformSampling实现对点云的统一重采样，具体通过建立点云的空间体素栅格，然后在此基础上实现下采样并且过滤一些数据。
// 所有采样后得到的点用每个体素内点集的重心近似，而不是用每个体素的中心点近似，前者速度较后者慢，但其估计的点更接近实际的采样面。
//
//pcl::PointCloud<int> sampled_indices;
pcl::UniformSampling<PointType> uniform_sampling;
uniform_sampling.setInputCloud (model); //输入点云
uniform_sampling.setRadiusSearch (model_ss_); //输入半径
//uniform_sampling.compute (sampled_indices);
//pcl::copyPointCloud (*model, sampled_indices.points, *model_keypoints);
uniform_sampling.filter(*model_keypoints); //滤波
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.compute (sampled_indices);
//pcl::copyPointCloud (*scene, sampled_indices.points, *scene_keypoints);
uniform_sampling.filter(*scene_keypoints);
std::cout << "Scene total points: " << scene->size () << "; Selected Keypoints: " << scene_keypoints->size () << std::endl;
//
// Compute Descriptor for keypoints：为关键点计算描述子
//
pcl::SHOTEstimationOMP<PointType, NormalType, DescriptorType> descr_est;
descr_est.setNumberOfThreads(4);
descr_est.setRadiusSearch (descr_rad_); //设置搜索半径
descr_est.setInputCloud (model_keypoints); //模型点云的关键点
descr_est.setInputNormals (model_normals); //模型点云的法线
descr_est.setSearchSurface (model); //模型点云
descr_est.compute (*model_descriptors); //计算描述子
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：使用Kdtree找出 Model-Scene 匹配点
//
pcl::CorrespondencesPtr model_scene_corrs (new pcl::Correspondences ());
pcl::KdTreeFLANN<DescriptorType> match_search; //设置配准方式
match_search.setInputCloud (model_descriptors);//模型点云的描述子
//每一个场景的关键点描述子都要找到模板中匹配的关键点描述子并将其添加到对应的匹配向量中。
for (size_t i = 0; i < scene_descriptors->size (); ++i)
{
std::vector<int> neigh_indices (1); //设置最近邻点的索引
std::vector<float> neigh_sqr_dists (1);//设置最近邻平方距离值
if (!pcl_isfinite (scene_descriptors->at (i).descriptor[0])) //忽略 NaNs点
{
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) //仅当描述子与临近点的平方距离小于0.25（描述子与临近的距离在一般在0到1之间）才添加匹配
{
//neigh_indices[0]给定点，i是配准数neigh_sqr_dists[0]与临近点的平方距离
pcl::Correspondence corr (neigh_indices[0], static_cast<int> (i), neigh_sqr_dists[0]);
model_scene_corrs->push_back (corr);//把配准的点存储在容器中
}
}
std::cout << "Correspondences found: " << model_scene_corrs->size () << std::endl;
//
// Actual Clustering：实际的配准方法的实现
//
std::vector<Eigen::Matrix4f, Eigen::aligned_allocator<Eigen::Matrix4f> > rototranslations;
std::vector<pcl::Correspondences> clustered_corrs;
// 使用 Hough3D算法寻找匹配点
if (use_hough_)
{
//
// Compute (Keypoints) Reference Frames only for Hough
//
//利用hough算法时，需要计算关键点的局部参考坐标系（LRF）
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_);//估计局部参考坐标系时当前点的邻域搜索半径
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_); //hough空间的采样间隔：0.01
clusterer.setHoughThreshold (cg_thresh_); //在hough空间确定是否有实例存在的最少票数阈值：5
clusterer.setUseInterpolation (true); //设置是否对投票在hough空间进行插值计算
clusterer.setUseDistanceWeight (false); //设置在投票时是否将对应点之间的距离作为权重参与计算
clusterer.setInputCloud (model_keypoints); //设置模型点云的关键点
clusterer.setInputRf (model_rf); //设置模型对应的局部坐标系
clusterer.setSceneCloud (scene_keypoints);
clusterer.setSceneRf (scene_rf);
clusterer.setModelSceneCorrespondences (model_scene_corrs);//设置模型与场景的对应点的集合
//clusterer.cluster (clustered_corrs);
clusterer.recognize (rototranslations, clustered_corrs); //结果包含变换矩阵和对应点聚类结果
}
else // Using GeometricConsistency:使用几何一致性性质
{
pcl::GeometricConsistencyGrouping<PointType, PointType> gc_clusterer;
gc_clusterer.setGCSize (cg_size_); //设置几何一致性的大小
gc_clusterer.setGCThreshold (cg_thresh_); //阈值
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：找出输入模型是否在场景中出现
//
std::cout << "Model instances found: " << rototranslations.size () << std::endl;
for (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;
//打印处相对于输入模型的旋转矩阵与平移矩阵
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 ("点云库PCL学习教程第二版-基于对应点聚类的3D模型识别");
viewer.addPointCloud (scene, "scene_cloud");//可视化场景点云
viewer.setBackgroundColor(255,255,255);
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_)//可视化配准点
{
//We are translating the model so that it doesn't end in the middle of the scene representation
//对输入的模型进行旋转与平移，使其在可视化界面的中间位置
pcl::transformPointCloud (*model, *off_scene_model, Eigen::Vector3f (0,0,0), 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, 0, 255, 0);
viewer.addPointCloud (off_scene_model, off_scene_model_color_handler, "off_scene_model");
}
if (show_keypoints_)//可视化关键点：蓝色
{
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 (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]);//把model转化为rotated_model
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_)//显示配准连接线
{
for (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);
}
Remarks:
If pcd with the book tape, when loading milk_pose_changed.pcd, will be reported ". Failed to find match for field 'rgba'" because this file does not include rgba information; the original file can be downloaded from the official website: https://github.com / PointCloudLibrary / pcl / tree / master / test
Excuting an order:
GC: Set --cg_thresh 10, excluding erroneous recognition model
.\correspondence_grouping.exe ..\..\source\milk_color.pcd ..\..\source\milk_cartoon_all_small_clorox-1.pcd --algorithm GC -k -c --cg_thresh 10The results show: Key (blue), the model instance (red) identified, the corresponding point on the connecting line (green)
Hough way:
.\correspondence_grouping.exe ..\..\source\milk_color.pcd ..\..\source\milk_cartoon_all_small_clorox-1.pcd --algorithm Houg
h -k -c