以相关组为基础的3D物体识别

这次我们要解释如何以pcl_recognition模块来进行3D物体识别。特别地，它解释了怎么使用相关组算法为了聚类那些从3D描述器算法里面把当前的场景与模型进行匹配的相关点对点的匹配。(长难句)。对于每一次聚类，描绘了一个在场景中的可能模型实例，相关组算法也输出标识6DOF位姿估计的转换矩阵。

代码

#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 (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> ());
  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 (size_t i = 0; i < scene_descriptors->size (); ++i)
  {
    std::vector<int> neigh_indices (1);
    std::vector<float> neigh_sqr_dists (1);
    if (!pcl_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 (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 (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 (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);
}

帮助函数

这个函数将会打印出几个命令行的简短选项的解释。

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;
}

第二个函数，解析命令行参数为了设置正确的参数来执行。

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_);
}

你可以用两个相应的命令行选项来进行聚类算法的切换。--algorithm(Hough|GC)

Hough(默认)

这是一个以3DHough决策为基础的算法

GC

这是一个geometric consistency(GC) 几何一致聚类算法，执行简单的几何约束在一对相关的物体之间。

一些别的比较有趣的选项 -k -c 和 -r

-k 显示了计算相关度的关键点，以蓝色覆盖的形式显示。

-c画了一条线连接两幅图里面相关的地方。

-r估计模型点的空间分辨率，然后考虑把半径作为点云分辨率。因此获取一些分辨率不变量将会是有用的，当我们获取同样的命令行从不同的点云里面。

下面这个函数实现了某个点云的空间分辨率计算，通过对每个点云里面的点和其最近邻平均距离。

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;
}

聚类通道

这是我们主要的函数，执行实际的聚类操作。

parseCommandLine (argc, argv);
  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);
  }

第二步，只有分辨率不变这个标志被enable,程序会调整会在下一节里面使用的半径，把它们和模型分辨率相乘。

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;
  }

接下去，将会计算模型和场景的每个点的法线，通过NormalEstiminationOMP估计器，使用10个最近邻。

  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);

接下去我们对每个点云降低取样来找到少量的关键点。UniformSampling里面要使用的参数要么是命令行里面进行设置要么就用默认值。

  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;

下一个阶段在于把3D描述器和每个模型和场景的点进行关联。在这里，我们将计算SHOT描述器，使用SHOTEstiminationOMP

  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);

现在我们需要决定模型描述器和场景间的相关性。为了做到这，这个程序使用了一个KdTreeFLANN。对于每一个和一个场景关键点相关联的描述器，它会以欧拉距离为基础找到最相似的模型描述器，并把这对相似的关键点放到Correspondences这个向量里面。

  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 (size_t i = 0; i < scene_descriptors->size (); ++i)
  {
    std::vector<int> neigh_indices (1);
    std::vector<float> neigh_sqr_dists (1);
    if (!pcl_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

最后的阶段是管道对前面相关的东西进行聚类。

默认的算法是Hough3DGrouping，这是一个以Hough决策为基础的算法。记住这个算法需要与一个Local Reference Frame对每一个作为参数传递的点云关键点。在这个例子里面，我们显示的计算了LRF通过BOARDLocalReferenceFrameEstimation这个预估器。

    //  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;

注意我们并于一定要显示的计算LRF在调用聚类算法前。如果被用来聚类的点云没用相关联的LRF。

Hough3DGrouping算法是可选的，我们还可以使用GeometricConsistencyGrouping这个算法。在这个例子里面LRF计算是不需要的，我们只要简单创建一个算法类的实例，传入相应的参数，并调用相应的recognize这个方法。

    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);
  }

输出和可视化。

    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));
  }

程序到时候会显示一个PCLVisualizer这个窗口。如果命令行选项里面-k或者-c被使用了，程序将会显示一个“独立的”经过渲染的点云。

 

  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 (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 (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);
}

运行程序

./correspondence_grouping milk.pcd milk_cartoon_all_small_clorox.pcd

可选的，你可以指定点云分辨率单元里面的半径

./correspondence_grouping milk.pcd milk_cartoon_all_small_clorox.pcd milk.pcd milk_cartoon_all_small_clorox.pcd -r --model_ss 7.5 --scene_ss 20 --rf_rad 10 --descr_rad 15 --cg_size 10

提示:如果你使用不同的点云并且你不知道如何去设置不同的参数，你可以使用"-r"这个标志还有设置LRF和描述器半径变成分辨率的5 10 15倍。接着你需要小心的调节参数来得到最好的结果。

几秒之后会有如下输出

Model total points: 13704; Selected Keypoints: 732
Scene total points: 307200; Selected Keypoints: 3747

Correspondences found: 1768
Model instances found: 1

  Instance 1:
    Correspondences belonging to this instance: 24

        |  0.969 -0.120  0.217 |
    R = |  0.117  0.993  0.026 |
        | -0.218 -0.000  0.976 |

    t = < -0.159, 0.212, -0.042 >

 最终提示，如果你的pcl/filters里面没有uniform_sampling.h这个文件你可以从https://github.com/PointCloudLibrary/pcl/tree/master/filters/include/pcl/filters下载uniform_sampling.h和impl里面的uniform_sampling.hpp放到/usr/include/pcl/pcl1.7里面对应的地方。