[总结]pcl 库 tutorial 参考官网汇总

第一部分主要是滤波

1.1对载入的点云通过VoxelGrid(体素滤波)进行降采样

在我的理解，有两个点集，source和target，target不变，source经过旋转（Rotation）和平移（Translation）甚至加上尺度（Scale）变换，使得变换后的source点集尽量和target点集重合，这个变换的过程就叫点集配准。

#include
#include
#include
#include
#include 

int main(int argc, const char** argv) {
     pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_in(new pcl::PointCloud<pcl::PointXYZ>);
     if (pcl::io::loadPLYFile<pcl::PointXYZ>("../data/geely.ply", *cloud_in) == -1) //* load the file 
    {
        PCL_ERROR("Couldn't read file test_pcd.pcd \n");
        system("PAUSE");
        return (-1);
    }
    std::cout<<"原始点云数量:"<<cloud_in->size()<<std::endl;
    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_out(new pcl::PointCloud<pcl::PointXYZ>);
    pcl::VoxelGrid<pcl::PointXYZ> sor;
    sor.setInputCloud(cloud_in);
    sor.setLeafSize(0.1f, 0.1f, 0.1f);//设置滤波器处理时采用的体素大小的参数
    //这里面越大,点云就越少
    sor.filter(*cloud_out);
    std::cout<<"滤波点云数量:"<<cloud_out->size()<<std::endl;
     pcl::PCDWriter writer;
  writer.write("grid.pcd",*cloud_out);
    return 0;
}

1.2pcl 平面模型分割
相关教程链接:
http://pointclouds.org/documentation/tutorials/planar_segmentation.php

相关示例代码

#include 
#include 
#include 
#include 
#include 
#include 
#include 

int
 main (int argc, char** argv)
{
  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);

  // Fill in the cloud data
  cloud->width  = 15;
  cloud->height = 1;
  cloud->points.resize (cloud->width * cloud->height);

  // Generate the data
  for (std::size_t i = 0; i < cloud->points.size (); ++i)
  {
    cloud->points[i].x = 1024 * rand () / (RAND_MAX + 1.0f);
    cloud->points[i].y = 1024 * rand () / (RAND_MAX + 1.0f);
    cloud->points[i].z = 1.0;
  }

  // Set a few outliers
  cloud->points[0].z = 2.0;
  cloud->points[3].z = -2.0;
  cloud->points[6].z = 4.0;

  std::cerr << "Point cloud data: " << cloud->points.size () << " points" << std::endl;
  for (std::size_t i = 0; i < cloud->points.size (); ++i)
    std::cerr << "    " << cloud->points[i].x << " "
                        << cloud->points[i].y << " "
                        << cloud->points[i].z << std::endl;

  pcl::ModelCoefficients::Ptr coefficients (new pcl::ModelCoefficients);
  pcl::PointIndices::Ptr inliers (new pcl::PointIndices);
  // Create the segmentation object
  pcl::SACSegmentation<pcl::PointXYZ> seg;
  // Optional
  seg.setOptimizeCoefficients (true);
  // Mandatory
  seg.setModelType (pcl::SACMODEL_PLANE);
  seg.setMethodType (pcl::SAC_RANSAC);
  seg.setDistanceThreshold (0.01);

  seg.setInputCloud (cloud);
  seg.segment (*inliers, *coefficients);

  if (inliers->indices.size () == 0)
  {
    PCL_ERROR ("Could not estimate a planar model for the given dataset.");
    return (-1);
  }

  std::cerr << "Model coefficients: " << coefficients->values[0] << " " 
                                      << coefficients->values[1] << " "
                                      << coefficients->values[2] << " " 
                                      << coefficients->values[3] << std::endl;

  std::cerr << "Model inliers: " << inliers->indices.size () << std::endl;
  for (std::size_t i = 0; i < inliers->indices.size (); ++i)
    std::cerr << inliers->indices[i] << "    " << cloud->points[inliers->indices[i]].x << " "
                                               << cloud->points[inliers->indices[i]].y << " "
                                               << cloud->points[inliers->indices[i]].z << std::endl;

  return (0);
}

1.3.pass through pcl进行直通滤波器,滤除一些噪点

#include 
#include 
#include 

int
 main (int argc, char** argv)
{
  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);
  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered (new pcl::PointCloud<pcl::PointXYZ>);

  // Fill in the cloud data
  cloud->width  = 5;
  cloud->height = 1;
  cloud->points.resize (cloud->width * cloud->height);

  for (std::size_t i = 0; i < cloud->points.size (); ++i)
  {
    cloud->points[i].x = 1024 * rand () / (RAND_MAX + 1.0f);
    cloud->points[i].y = 1024 * rand () / (RAND_MAX + 1.0f);
    cloud->points[i].z = 1024 * rand () / (RAND_MAX + 1.0f);
  }

  std::cerr << "Cloud before filtering: " << std::endl;
  for (std::size_t i = 0; i < cloud->points.size (); ++i)
    std::cerr << "    " << cloud->points[i].x << " " 
                        << cloud->points[i].y << " " 
                        << cloud->points[i].z << std::endl;

  // Create the filtering object
  pcl::PassThrough<pcl::PointXYZ> pass;
  pass.setInputCloud (cloud);
  pass.setFilterFieldName ("z");
  pass.setFilterLimits (0.0, 1.0);
  //pass.setFilterLimitsNegative (true);
  pass.filter (*cloud_filtered);

  std::cerr << "Cloud after filtering: " << std::endl;
  for (std::size_t i = 0; i < cloud_filtered->points.size (); ++i)
    std::cerr << "    " << cloud_filtered->points[i].x << " " 
                        << cloud_filtered->points[i].y << " " 
                        << cloud_filtered->points[i].z << std::endl;

  return (0);
}

1.4.pcl使用StatisticalOutlierRemoval 来滤除一些噪点

#include 
#include 
#include 
#include 

int
main (int argc, char** argv)
{
  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);
  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered (new pcl::PointCloud<pcl::PointXYZ>);

  // Fill in the cloud data
  pcl::PCDReader reader;
  // Replace the path below with the path where you saved your file
  reader.read<pcl::PointXYZ> ("table_scene_lms400.pcd", *cloud);

  std::cerr << "Cloud before filtering: " << std::endl;
  std::cerr << *cloud << std::endl;

  // Create the filtering object
  pcl::StatisticalOutlierRemoval<pcl::PointXYZ> sor;
  sor.setInputCloud (cloud);
  sor.setMeanK (50);
  sor.setStddevMulThresh (1.0);
  sor.filter (*cloud_filtered);

  std::cerr << "Cloud after filtering: " << std::endl;
  std::cerr << *cloud_filtered << std::endl;

  pcl::PCDWriter writer;
  writer.write<pcl::PointXYZ> ("table_scene_lms400_inliers.pcd", *cloud_filtered, false);

  sor.setNegative (true);
  sor.filter (*cloud_filtered);
  writer.write<pcl::PointXYZ> ("table_scene_lms400_outliers.pcd", *cloud_filtered, false);

  return (0);
}

1.5将所有的点都投影到一个平面上

#include 
#include 
#include 
#include 
#include 

int
 main (int argc, char** argv)
{
  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);
  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_projected (new pcl::PointCloud<pcl::PointXYZ>);

  // Fill in the cloud data
  cloud->width  = 5;
  cloud->height = 1;
  cloud->points.resize (cloud->width * cloud->height);

  for (std::size_t i = 0; i < cloud->points.size (); ++i)
  {
    cloud->points[i].x = 1024 * rand () / (RAND_MAX + 1.0f);
    cloud->points[i].y = 1024 * rand () / (RAND_MAX + 1.0f);
    cloud->points[i].z = 1024 * rand () / (RAND_MAX + 1.0f);
  }

  std::cerr << "Cloud before projection: " << std::endl;
  for (std::size_t i = 0; i < cloud->points.size (); ++i)
    std::cerr << "    " << cloud->points[i].x << " " 
                        << cloud->points[i].y << " " 
                        << cloud->points[i].z << std::endl;

  // Create a set of planar coefficients with X=Y=0,Z=1
//  我们填写ModelCoefficients值。在这种情况下，我们使用一个平面模型，其中ax + by + cz + d = 0，其中a = b = d = 0，c = 1，或者换句话说，XY平面。
  pcl::ModelCoefficients::Ptr coefficients (new pcl::ModelCoefficients ());
  coefficients->values.resize (4);
  coefficients->values[0] = coefficients->values[1] = 0;
  coefficients->values[2] = 1.0;
  coefficients->values[3] = 0;

  // Create the filtering object
  pcl::ProjectInliers<pcl::PointXYZ> proj;
  proj.setModelType (pcl::SACMODEL_PLANE);
  proj.setInputCloud (cloud);
  proj.setModelCoefficients (coefficients);
  proj.filter (*cloud_projected);

  std::cerr << "Cloud after projection: " << std::endl;
  for (std::size_t i = 0; i < cloud_projected->points.size (); ++i)
    std::cerr << "    " << cloud_projected->points[i].x << " " 
                        << cloud_projected->points[i].y << " " 
                        << cloud_projected->points[i].z << std::endl;

  return (0);
}

1.6从点云中提取索引(说实话,我也没怎么看懂)

#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 

int
main (int argc, char** argv)
{
  pcl::PCLPointCloud2::Ptr cloud_blob (new pcl::PCLPointCloud2), cloud_filtered_blob (new pcl::PCLPointCloud2);
  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered (new pcl::PointCloud<pcl::PointXYZ>), cloud_p (new pcl::PointCloud<pcl::PointXYZ>), cloud_f (new pcl::PointCloud<pcl::PointXYZ>);

  // Fill in the cloud data
  pcl::PCDReader reader;
  reader.read ("table_scene_lms400.pcd", *cloud_blob);

  std::cerr << "PointCloud before filtering: " << cloud_blob->width * cloud_blob->height << " data points." << std::endl;

  // Create the filtering object: downsample the dataset using a leaf size of 1cm
  pcl::VoxelGrid<pcl::PCLPointCloud2> sor;
  sor.setInputCloud (cloud_blob);
  sor.setLeafSize (0.01f, 0.01f, 0.01f);
  sor.filter (*cloud_filtered_blob);

  // Convert to the templated PointCloud
  pcl::fromPCLPointCloud2 (*cloud_filtered_blob, *cloud_filtered);

  std::cerr << "PointCloud after filtering: " << cloud_filtered->width * cloud_filtered->height << " data points." << std::endl;

  // Write the downsampled version to disk
  pcl::PCDWriter writer;
  writer.write<pcl::PointXYZ> ("table_scene_lms400_downsampled.pcd", *cloud_filtered, false);

  pcl::ModelCoefficients::Ptr coefficients (new pcl::ModelCoefficients ());
  pcl::PointIndices::Ptr inliers (new pcl::PointIndices ());
  // Create the segmentation object
  pcl::SACSegmentation<pcl::PointXYZ> seg;
  // Optional
  seg.setOptimizeCoefficients (true);
  // Mandatory
  seg.setModelType (pcl::SACMODEL_PLANE);
  seg.setMethodType (pcl::SAC_RANSAC);
  seg.setMaxIterations (1000);
  seg.setDistanceThreshold (0.01);

  // Create the filtering object
  pcl::ExtractIndices<pcl::PointXYZ> extract;

  int i = 0, nr_points = (int) cloud_filtered->points.size ();
  // While 30% of the original cloud is still there
  while (cloud_filtered->points.size () > 0.3 * nr_points)
  {
    // Segment the largest planar component from the remaining cloud
    seg.setInputCloud (cloud_filtered);
    seg.segment (*inliers, *coefficients);
    if (inliers->indices.size () == 0)
    {
      std::cerr << "Could not estimate a planar model for the given dataset." << std::endl;
      break;
    }

    // Extract the inliers
    extract.setInputCloud (cloud_filtered);
    extract.setIndices (inliers);
    extract.setNegative (false);
    extract.filter (*cloud_p);
    std::cerr << "PointCloud representing the planar component: " << cloud_p->width * cloud_p->height << " data points." << std::endl;

    std::stringstream ss;
    ss << "table_scene_lms400_plane_" << i << ".pcd";
    writer.write<pcl::PointXYZ> (ss.str (), *cloud_p, false);

    // Create the filtering object
    extract.setNegative (true);
    extract.filter (*cloud_f);
    cloud_filtered.swap (cloud_f);
    i++;
  }

  return (0);
}

1.7 使用ConditionalRemoval过滤器，该过滤器将删除给定输入云中不满足一个或多个给定条件的所有索引。然后使用RadiusOutlierRemoval过滤器，该过滤器将删除输入云中在一定范围内没有至少一些邻居的所有索引

创建RadiusOutlierRemoval过滤器对象设置其参数并将其应用于输入云。搜索半径设置为0.8，并且一个点在该半径内必须至少有2个邻居才能保留为PointCloud的一部分。

#include 
#include 
#include 
#include 

int
 main (int argc, char** argv)
{
  if (argc != 2)
  {
    std::cerr << "please specify command line arg '-r' or '-c'" << std::endl;
    exit(0);
  }
  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);
  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered (new pcl::PointCloud<pcl::PointXYZ>);

  // Fill in the cloud data
  cloud->width  = 5;
  cloud->height = 1;
  cloud->points.resize (cloud->width * cloud->height);

  for (std::size_t i = 0; i < cloud->points.size (); ++i)
  {
    cloud->points[i].x = 1024 * rand () / (RAND_MAX + 1.0f);
    cloud->points[i].y = 1024 * rand () / (RAND_MAX + 1.0f);
    cloud->points[i].z = 1024 * rand () / (RAND_MAX + 1.0f);
  }

  if (strcmp(argv[1], "-r") == 0){
    pcl::RadiusOutlierRemoval<pcl::PointXYZ> outrem;
    // build the filter
    outrem.setInputCloud(cloud);
    outrem.setRadiusSearch(0.8);
    outrem.setMinNeighborsInRadius (2);
    // apply filter
    outrem.filter (*cloud_filtered);
  }
  else if (strcmp(argv[1], "-c") == 0){
    // build the condition
    pcl::ConditionAnd<pcl::PointXYZ>::Ptr range_cond (new
      pcl::ConditionAnd<pcl::PointXYZ> ());
    range_cond->addComparison (pcl::FieldComparison<pcl::PointXYZ>::ConstPtr (new
      pcl::FieldComparison<pcl::PointXYZ> ("z", pcl::ComparisonOps::GT, 0.0)));
    range_cond->addComparison (pcl::FieldComparison<pcl::PointXYZ>::ConstPtr (new
      pcl::FieldComparison<pcl::PointXYZ> ("z", pcl::ComparisonOps::LT, 0.8)));
    // build the filter
    pcl::ConditionalRemoval<pcl::PointXYZ> condrem;
    condrem.setCondition (range_cond);
    condrem.setInputCloud (cloud);
    condrem.setKeepOrganized(true);
    // apply filter
    condrem.filter (*cloud_filtered);
  }
  else{
    std::cerr << "please specify command line arg '-r' or '-c'" << std::endl;
    exit(0);
  }
  std::cerr << "Cloud before filtering: " << std::endl;
  for (std::size_t i = 0; i < cloud->points.size (); ++i)
    std::cerr << "    " << cloud->points[i].x << " "
                        << cloud->points[i].y << " "
                        << cloud->points[i].z << std::endl;
  // display pointcloud after filtering
  std::cerr << "Cloud after filtering: " << std::endl;
  for (std::size_t i = 0; i < cloud_filtered->points.size (); ++i)
    std::cerr << "    " << cloud_filtered->points[i].x << " "
                        << cloud_filtered->points[i].y << " "
                        << cloud_filtered->points[i].z << std::endl;
  return (0);
}

提取关键点

/* \author Bastian Steder */

#include 

#include 
#include 
#include 
#include 
#include 
#include 
#include 

typedef pcl::PointXYZ PointType;

// --------------------
// -----Parameters-----
// --------------------
float angular_resolution = 0.5f;
float support_size = 0.2f;
pcl::RangeImage::CoordinateFrame coordinate_frame = pcl::RangeImage::CAMERA_FRAME;
bool setUnseenToMaxRange = false;

// --------------
// -----Help-----
// --------------
void 
printUsage (const char* progName)
{
  std::cout << "\n\nUsage: "<<progName<<" [options] \n\n"
            << "Options:\n"
            << "-------------------------------------------\n"
            << "-r    angular resolution in degrees (default "<<angular_resolution<<")\n"
            << "-c      coordinate frame (default "<< (int)coordinate_frame<<")\n"
            << "-m           Treat all unseen points as maximum range readings\n"
            << "-s    support size for the interest points (diameter of the used sphere - "
            <<                                                     "default "<<support_size<<")\n"
            << "-h           this help\n"
            << "\n\n";
}

//void 
//setViewerPose (pcl::visualization::PCLVisualizer& viewer, const Eigen::Affine3f& viewer_pose)
//{
  //Eigen::Vector3f pos_vector = viewer_pose * Eigen::Vector3f (0, 0, 0);
  //Eigen::Vector3f look_at_vector = viewer_pose.rotation () * Eigen::Vector3f (0, 0, 1) + pos_vector;
  //Eigen::Vector3f up_vector = viewer_pose.rotation () * Eigen::Vector3f (0, -1, 0);
  //viewer.setCameraPosition (pos_vector[0], pos_vector[1], pos_vector[2],
                            //look_at_vector[0], look_at_vector[1], look_at_vector[2],
                            //up_vector[0], up_vector[1], up_vector[2]);
//}

// --------------
// -----Main-----
// --------------
int 
main (int argc, char** argv)
{
  // --------------------------------------
  // -----Parse Command Line Arguments-----
  // --------------------------------------
  if (pcl::console::find_argument (argc, argv, "-h") >= 0)
  {
    printUsage (argv[0]);
    return 0;
  }
  if (pcl::console::find_argument (argc, argv, "-m") >= 0)
  {
    setUnseenToMaxRange = true;
    std::cout << "Setting unseen values in range image to maximum range readings.\n";
  }
  int tmp_coordinate_frame;
  if (pcl::console::parse (argc, argv, "-c", tmp_coordinate_frame) >= 0)
  {
    coordinate_frame = pcl::RangeImage::CoordinateFrame (tmp_coordinate_frame);
    std::cout << "Using coordinate frame "<< (int)coordinate_frame<<".\n";
  }
  if (pcl::console::parse (argc, argv, "-s", support_size) >= 0)
    std::cout << "Setting support size to "<<support_size<<".\n";
  if (pcl::console::parse (argc, argv, "-r", angular_resolution) >= 0)
    std::cout << "Setting angular resolution to "<<angular_resolution<<"deg.\n";
  angular_resolution = pcl::deg2rad (angular_resolution);
  
  // ------------------------------------------------------------------
  // -----Read pcd file or create example point cloud if not given-----
  // ------------------------------------------------------------------
  pcl::PointCloud<PointType>::Ptr point_cloud_ptr (new pcl::PointCloud<PointType>);
  pcl::PointCloud<PointType>& point_cloud = *point_cloud_ptr;
  pcl::PointCloud<pcl::PointWithViewpoint> far_ranges;
  Eigen::Affine3f scene_sensor_pose (Eigen::Affine3f::Identity ());
  std::vector<int> pcd_filename_indices = pcl::console::parse_file_extension_argument (argc, argv, "pcd");
  if (!pcd_filename_indices.empty ())
  {
    std::string filename = argv[pcd_filename_indices[0]];
    if (pcl::io::loadPCDFile (filename, point_cloud) == -1)
    {
      std::cerr << "Was not able to open file \""<<filename<<"\".\n";
      printUsage (argv[0]);
      return 0;
    }
    scene_sensor_pose = Eigen::Affine3f (Eigen::Translation3f (point_cloud.sensor_origin_[0],
                                                               point_cloud.sensor_origin_[1],
                                                               point_cloud.sensor_origin_[2])) *
                        Eigen::Affine3f (point_cloud.sensor_orientation_);
    std::string far_ranges_filename = pcl::getFilenameWithoutExtension (filename)+".pcd";
    if (pcl::io::loadPCDFile (far_ranges_filename.c_str (), far_ranges) == -1)
      std::cout << "Far ranges file \""<<far_ranges_filename<<"\" does not exists.\n";
  }
  else
  {
    setUnseenToMaxRange = true;
    std::cout << "\nNo *.pcd file given => Generating example point cloud.\n\n";
    for (float x=-0.5f; x<=0.5f; x+=0.01f)
    {
      for (float y=-0.5f; y<=0.5f; y+=0.01f)
      {
        PointType point;  point.x = x;  point.y = y;  point.z = 2.0f - y;
        point_cloud.points.push_back (point);
      }
    }
    point_cloud.width = (int) point_cloud.points.size ();  point_cloud.height = 1;
  }
  
  // -----------------------------------------------
  // -----Create RangeImage from the PointCloud-----
  // -----------------------------------------------
  float noise_level = 0.0;
  float min_range = 0.0f;
  int border_size = 1;
  pcl::RangeImage::Ptr range_image_ptr (new pcl::RangeImage);
  pcl::RangeImage& range_image = *range_image_ptr;   
  range_image.createFromPointCloud (point_cloud, angular_resolution, pcl::deg2rad (360.0f), pcl::deg2rad (180.0f),
                                   scene_sensor_pose, coordinate_frame, noise_level, min_range, border_size);
  range_image.integrateFarRanges (far_ranges);
  if (setUnseenToMaxRange)
    range_image.setUnseenToMaxRange ();
  
  // --------------------------------------------
  // -----Open 3D viewer and add point cloud-----
  // --------------------------------------------
  pcl::visualization::PCLVisualizer viewer ("3D Viewer");
  viewer.setBackgroundColor (1, 1, 1);
  pcl::visualization::PointCloudColorHandlerCustom<pcl::PointWithRange> range_image_color_handler (range_image_ptr, 0, 0, 0);
  viewer.addPointCloud (range_image_ptr, range_image_color_handler, "range image");
  viewer.setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 1, "range image");
  //viewer.addCoordinateSystem (1.0f, "global");
  //PointCloudColorHandlerCustom point_cloud_color_handler (point_cloud_ptr, 150, 150, 150);
  //viewer.addPointCloud (point_cloud_ptr, point_cloud_color_handler, "original point cloud");
  viewer.initCameraParameters ();
  //setViewerPose (viewer, range_image.getTransformationToWorldSystem ());
  
  // --------------------------
  // -----Show range image-----
  // --------------------------
  pcl::visualization::RangeImageVisualizer range_image_widget ("Range image");
  range_image_widget.showRangeImage (range_image);
  
  // --------------------------------
  // -----Extract NARF keypoints-----
  // --------------------------------
  pcl::RangeImageBorderExtractor range_image_border_extractor;
  pcl::NarfKeypoint narf_keypoint_detector (&range_image_border_extractor);
  narf_keypoint_detector.setRangeImage (&range_image);
  narf_keypoint_detector.getParameters ().support_size = support_size;
  //narf_keypoint_detector.getParameters ().add_points_on_straight_edges = true;
  //narf_keypoint_detector.getParameters ().distance_for_additional_points = 0.5;
  
  pcl::PointCloud<int> keypoint_indices;
  narf_keypoint_detector.compute (keypoint_indices);
  std::cout << "Found "<<keypoint_indices.points.size ()<<" key points.\n";

  // ----------------------------------------------
  // -----Show keypoints in range image widget-----
  // ----------------------------------------------
  //for (std::size_t i=0; i
    //range_image_widget.markPoint (keypoint_indices.points[i]%range_image.width,
                                  //keypoint_indices.points[i]/range_image.width);
  
  // -------------------------------------
  // -----Show keypoints in 3D viewer-----
  // -------------------------------------
  pcl::PointCloud<pcl::PointXYZ>::Ptr keypoints_ptr (new pcl::PointCloud<pcl::PointXYZ>);
  pcl::PointCloud<pcl::PointXYZ>& keypoints = *keypoints_ptr;
  keypoints.points.resize (keypoint_indices.points.size ());
  for (std::size_t i=0; i<keypoint_indices.points.size (); ++i)
    keypoints.points[i].getVector3fMap () = range_image.points[keypoint_indices.points[i]].getVector3fMap ();

  pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> keypoints_color_handler (keypoints_ptr, 0, 255, 0);
  viewer.addPointCloud<pcl::PointXYZ> (keypoints_ptr, keypoints_color_handler, "keypoints");
  viewer.setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 7, "keypoints");
  
  //--------------------
  // -----Main loop-----
  //--------------------
  while (!viewer.wasStopped ())
  {
    range_image_widget.spinOnce ();  // process GUI events
    viewer.spinOnce ();
    pcl_sleep(0.01);
  }
}

KdTree查找特定点或位置的K个最近邻居，找到用户指定半径内的所有邻居.
KdTree它是一个二叉搜索树

#include 
#include 

#include 
#include 
#include 

int
main (int argc, char** argv)
{
  srand (time (NULL));

  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);

  // Generate pointcloud data
  cloud->width = 1000;
  cloud->height = 1;
  cloud->points.resize (cloud->width * cloud->height);

  for (std::size_t i = 0; i < cloud->points.size (); ++i)
  {
    cloud->points[i].x = 1024.0f * rand () / (RAND_MAX + 1.0f);
    cloud->points[i].y = 1024.0f * rand () / (RAND_MAX + 1.0f);
    cloud->points[i].z = 1024.0f * rand () / (RAND_MAX + 1.0f);
  }

  pcl::KdTreeFLANN<pcl::PointXYZ> kdtree;

  kdtree.setInputCloud (cloud);

  pcl::PointXYZ searchPoint;

  searchPoint.x = 1024.0f * rand () / (RAND_MAX + 1.0f);
  searchPoint.y = 1024.0f * rand () / (RAND_MAX + 1.0f);
  searchPoint.z = 1024.0f * rand () / (RAND_MAX + 1.0f);

  // K nearest neighbor search

  int K = 10;

  std::vector<int> pointIdxNKNSearch(K);
  std::vector<float> pointNKNSquaredDistance(K);

  std::cout << "K nearest neighbor search at (" << searchPoint.x 
            << " " << searchPoint.y 
            << " " << searchPoint.z
            << ") with K=" << K << std::endl;

  if ( kdtree.nearestKSearch (searchPoint, K, pointIdxNKNSearch, pointNKNSquaredDistance) > 0 )
  {
    for (std::size_t i = 0; i < pointIdxNKNSearch.size (); ++i)
      std::cout << "    "  <<   cloud->points[ pointIdxNKNSearch[i] ].x 
                << " " << cloud->points[ pointIdxNKNSearch[i] ].y 
                << " " << cloud->points[ pointIdxNKNSearch[i] ].z 
                << " (squared distance: " << pointNKNSquaredDistance[i] << ")" << std::endl;
  }

  // Neighbors within radius search

  std::vector<int> pointIdxRadiusSearch;
  std::vector<float> pointRadiusSquaredDistance;

  float radius = 256.0f * rand () / (RAND_MAX + 1.0f);

  std::cout << "Neighbors within radius search at (" << searchPoint.x 
            << " " << searchPoint.y 
            << " " << searchPoint.z
            << ") with radius=" << radius << std::endl;


  if ( kdtree.radiusSearch (searchPoint, radius, pointIdxRadiusSearch, pointRadiusSquaredDistance) > 0 )
  {
    for (std::size_t i = 0; i < pointIdxRadiusSearch.size (); ++i)
      std::cout << "    "  <<   cloud->points[ pointIdxRadiusSearch[i] ].x 
                << " " << cloud->points[ pointIdxRadiusSearch[i] ].y 
                << " " << cloud->points[ pointIdxRadiusSearch[i] ].z 
                << " (squared distance: " << pointRadiusSquaredDistance[i] << ")" << std::endl;
  }


  return 0;
}

octree八叉树实现的是点云的压缩,需要openni的设备

#include 
#include 
#include 
#include 

#include 

#include 
#include 
#include 

#ifdef WIN32
# define sleep(x) Sleep((x)*1000)
#endif

class SimpleOpenNIViewer
{
public:
  SimpleOpenNIViewer () :
    viewer (" Point Cloud Compression Example")
  {
  }

  void
  cloud_cb_ (const pcl::PointCloud<pcl::PointXYZRGBA>::ConstPtr &cloud)
  {
    if (!viewer.wasStopped ())
    {
      // stringstream to store compressed point cloud
      std::stringstream compressedData;
      // output pointcloud
      pcl::PointCloud<pcl::PointXYZRGBA>::Ptr cloudOut (new pcl::PointCloud<pcl::PointXYZRGBA> ());

      // compress point cloud
      PointCloudEncoder->encodePointCloud (cloud, compressedData);

      // decompress point cloud
      PointCloudDecoder->decodePointCloud (compressedData, cloudOut);


      // show decompressed point cloud
      viewer.showCloud (cloudOut);
    }
  }

  void
  run ()
  {

    bool showStatistics = true;

    // for a full list of profiles see: /io/include/pcl/compression/compression_profiles.h
    pcl::io::compression_Profiles_e compressionProfile = pcl::io::MED_RES_ONLINE_COMPRESSION_WITH_COLOR;

    // instantiate point cloud compression for encoding and decoding
    PointCloudEncoder = new pcl::io::OctreePointCloudCompression<pcl::PointXYZRGBA> (compressionProfile, showStatistics);
    PointCloudDecoder = new pcl::io::OctreePointCloudCompression<pcl::PointXYZRGBA> ();

    // create a new grabber for OpenNI devices
    pcl::Grabber* interface = new pcl::OpenNIGrabber ();

    // make callback function from member function
    std::function<void(const pcl::PointCloud<pcl::PointXYZRGBA>::ConstPtr&)> f =
      [this] (const pcl::PointCloud<pcl::PointXYZRGBA>::ConstPtr& cloud) { cloud_cb_ (cloud); };

    // connect callback function for desired signal. In this case its a point cloud with color values
    boost::signals2::connection c = interface->registerCallback (f);

    // start receiving point clouds
    interface->start ();

    while (!viewer.wasStopped ())
    {
      sleep (1);
    }

    interface->stop ();

    // delete point cloud compression instances
    delete (PointCloudEncoder);
    delete (PointCloudDecoder);

  }

  pcl::visualization::CloudViewer viewer;

  pcl::io::OctreePointCloudCompression<pcl::PointXYZRGBA>* PointCloudEncoder;
  pcl::io::OctreePointCloudCompression<pcl::PointXYZRGBA>* PointCloudDecoder;

};

int
main (int argc, char **argv)
{
  SimpleOpenNIViewer v;
  v.run ();

  return (0);
}

使用八叉树进行空间的分割和区域搜索

#include 
#include 

#include 
#include 
#include 

int
main (int argc, char** argv)
{
  srand ((unsigned int) time (NULL));

  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);

  // Generate pointcloud data
  cloud->width = 1000;
  cloud->height = 1;
  cloud->points.resize (cloud->width * cloud->height);

  for (std::size_t i = 0; i < cloud->points.size (); ++i)
  {
    cloud->points[i].x = 1024.0f * rand () / (RAND_MAX + 1.0f);
    cloud->points[i].y = 1024.0f * rand () / (RAND_MAX + 1.0f);
    cloud->points[i].z = 1024.0f * rand () / (RAND_MAX + 1.0f);
  }

  float resolution = 128.0f;

  pcl::octree::OctreePointCloudSearch<pcl::PointXYZ> octree (resolution);

  octree.setInputCloud (cloud);
  octree.addPointsFromInputCloud ();

  pcl::PointXYZ searchPoint;

  searchPoint.x = 1024.0f * rand () / (RAND_MAX + 1.0f);
  searchPoint.y = 1024.0f * rand () / (RAND_MAX + 1.0f);
  searchPoint.z = 1024.0f * rand () / (RAND_MAX + 1.0f);

  // Neighbors within voxel search

  std::vector<int> pointIdxVec;

  if (octree.voxelSearch (searchPoint, pointIdxVec))
  {
    std::cout << "Neighbors within voxel search at (" << searchPoint.x 
     << " " << searchPoint.y 
     << " " << searchPoint.z << ")" 
     << std::endl;
              
    for (std::size_t i = 0; i < pointIdxVec.size (); ++i)
   std::cout << "    " << cloud->points[pointIdxVec[i]].x 
       << " " << cloud->points[pointIdxVec[i]].y 
       << " " << cloud->points[pointIdxVec[i]].z << std::endl;
  }

  // K nearest neighbor search

  int K = 10;

  std::vector<int> pointIdxNKNSearch;
  std::vector<float> pointNKNSquaredDistance;

  std::cout << "K nearest neighbor search at (" << searchPoint.x 
            << " " << searchPoint.y 
            << " " << searchPoint.z
            << ") with K=" << K << std::endl;

  if (octree.nearestKSearch (searchPoint, K, pointIdxNKNSearch, pointNKNSquaredDistance) > 0)
  {
    for (std::size_t i = 0; i < pointIdxNKNSearch.size (); ++i)
      std::cout << "    "  <<   cloud->points[ pointIdxNKNSearch[i] ].x 
                << " " << cloud->points[ pointIdxNKNSearch[i] ].y 
                << " " << cloud->points[ pointIdxNKNSearch[i] ].z 
                << " (squared distance: " << pointNKNSquaredDistance[i] << ")" << std::endl;
  }

  // Neighbors within radius search

  std::vector<int> pointIdxRadiusSearch;
  std::vector<float> pointRadiusSquaredDistance;

  float radius = 256.0f * rand () / (RAND_MAX + 1.0f);

  std::cout << "Neighbors within radius search at (" << searchPoint.x 
      << " " << searchPoint.y 
      << " " << searchPoint.z
      << ") with radius=" << radius << std::endl;


  if (octree.radiusSearch (searchPoint, radius, pointIdxRadiusSearch, pointRadiusSquaredDistance) > 0)
  {
    for (std::size_t i = 0; i < pointIdxRadiusSearch.size (); ++i)
      std::cout << "    "  <<   cloud->points[ pointIdxRadiusSearch[i] ].x 
                << " " << cloud->points[ pointIdxRadiusSearch[i] ].y 
                << " " << cloud->points[ pointIdxRadiusSearch[i] ].z 
                << " (squared distance: " << pointRadiusSquaredDistance[i] << ")" << std::endl;
  }

}

使用八叉树检测出那些点具有空间上的变换

#include 
#include 

#include 
#include 
#include 

int
main (int argc, char** argv)
{
  srand ((unsigned int) time (NULL));

  // Octree resolution - side length of octree voxels
  float resolution = 32.0f;

  // Instantiate octree-based point cloud change detection class
  pcl::octree::OctreePointCloudChangeDetector<pcl::PointXYZ> octree (resolution);

  pcl::PointCloud<pcl::PointXYZ>::Ptr cloudA (new pcl::PointCloud<pcl::PointXYZ> );

  // Generate pointcloud data for cloudA
  cloudA->width = 128;
  cloudA->height = 1;
  cloudA->points.resize (cloudA->width * cloudA->height);

  for (std::size_t i = 0; i < cloudA->points.size (); ++i)
  {
    cloudA->points[i].x = 64.0f * rand () / (RAND_MAX + 1.0f);
    cloudA->points[i].y = 64.0f * rand () / (RAND_MAX + 1.0f);
    cloudA->points[i].z = 64.0f * rand () / (RAND_MAX + 1.0f);
  }

  // Add points from cloudA to octree
  octree.setInputCloud (cloudA);
  octree.addPointsFromInputCloud ();

  // Switch octree buffers: This resets octree but keeps previous tree structure in memory.
  octree.switchBuffers ();

  pcl::PointCloud<pcl::PointXYZ>::Ptr cloudB (new pcl::PointCloud<pcl::PointXYZ> );
   
  // Generate pointcloud data for cloudB 
  cloudB->width = 128;
  cloudB->height = 1;
  cloudB->points.resize (cloudB->width * cloudB->height);

  for (std::size_t i = 0; i < cloudB->points.size (); ++i)
  {
    cloudB->points[i].x = 64.0f * rand () / (RAND_MAX + 1.0f);
    cloudB->points[i].y = 64.0f * rand () / (RAND_MAX + 1.0f);
    cloudB->points[i].z = 64.0f * rand () / (RAND_MAX + 1.0f);
  }

  // Add points from cloudB to octree
  octree.setInputCloud (cloudB);
  octree.addPointsFromInputCloud ();

  std::vector<int> newPointIdxVector;

  // Get vector of point indices from octree voxels which did not exist in previous buffer
  octree.getPointIndicesFromNewVoxels (newPointIdxVector);

  // Output points
  std::cout << "Output from getPointIndicesFromNewVoxels:" << std::endl;
  for (std::size_t i = 0; i < newPointIdxVector.size (); ++i)
    std::cout << i << "# Index:" << newPointIdxVector[i]
              << "  Point:" << cloudB->points[newPointIdxVector[i]].x << " "
              << cloudB->points[newPointIdxVector[i]].y << " "
              << cloudB->points[newPointIdxVector[i]].z << std::endl;

}

从点云中创建range image

#include 

int main (int argc, char** argv) {
  pcl::PointCloud<pcl::PointXYZ> pointCloud;
  
  // Generate the data
  for (float y=-0.5f; y<=0.5f; y+=0.01f) {
    for (float z=-0.5f; z<=0.5f; z+=0.01f) {
      pcl::PointXYZ point;
      point.x = 2.0f - y;
      point.y = y;
      point.z = z;
      pointCloud.points.push_back(point);
    }
  }
  pointCloud.width = (std::uint32_t) pointCloud.points.size();
  pointCloud.height = 1;
  
  // We now want to create a range image from the above point cloud, with a 1deg angular resolution
  float angularResolution = (float) (  1.0f * (M_PI/180.0f));  //   1.0 degree in radians
  float maxAngleWidth     = (float) (360.0f * (M_PI/180.0f));  // 360.0 degree in radians
  float maxAngleHeight    = (float) (180.0f * (M_PI/180.0f));  // 180.0 degree in radians
  Eigen::Affine3f sensorPose = (Eigen::Affine3f)Eigen::Translation3f(0.0f, 0.0f, 0.0f);
  pcl::RangeImage::CoordinateFrame coordinate_frame = pcl::RangeImage::CAMERA_FRAME;
  float noiseLevel=0.00;
  float minRange = 0.0f;
  int borderSize = 1;
  
  pcl::RangeImage rangeImage;
  rangeImage.createFromPointCloud(pointCloud, angularResolution, maxAngleWidth, maxAngleHeight,
                                  sensorPose, coordinate_frame, noiseLevel, minRange, borderSize);
  
  std::cout << rangeImage << "\n";
}

检测出点云图像的边界

#include 

#include 
#include 
#include 
#include 
#include 
#include 

typedef pcl::PointXYZ PointType;

// --------------------
// -----Parameters-----
// --------------------
float angular_resolution = 0.5f;
pcl::RangeImage::CoordinateFrame coordinate_frame = pcl::RangeImage::CAMERA_FRAME;
bool setUnseenToMaxRange = false;

// --------------
// -----Help-----
// --------------
void 
printUsage (const char* progName)
{
  std::cout << "\n\nUsage: "<<progName<<" [options] \n\n"
            << "Options:\n"
            << "-------------------------------------------\n"
            << "-r    angular resolution in degrees (default "<<angular_resolution<<")\n"
            << "-c      coordinate frame (default "<< (int)coordinate_frame<<")\n"
            << "-m           Treat all unseen points to max range\n"
            << "-h           this help\n"
            << "\n\n";
}

// --------------
// -----Main-----
// --------------
int 
main (int argc, char** argv)
{
  // --------------------------------------
  // -----Parse Command Line Arguments-----
  // --------------------------------------
  if (pcl::console::find_argument (argc, argv, "-h") >= 0)
  {
    printUsage (argv[0]);
    return 0;
  }
  if (pcl::console::find_argument (argc, argv, "-m") >= 0)
  {
    setUnseenToMaxRange = true;
    std::cout << "Setting unseen values in range image to maximum range readings.\n";
  }
  int tmp_coordinate_frame;
  if (pcl::console::parse (argc, argv, "-c", tmp_coordinate_frame) >= 0)
  {
    coordinate_frame = pcl::RangeImage::CoordinateFrame (tmp_coordinate_frame);
    std::cout << "Using coordinate frame "<< (int)coordinate_frame<<".\n";
  }
  if (pcl::console::parse (argc, argv, "-r", angular_resolution) >= 0)
    std::cout << "Setting angular resolution to "<<angular_resolution<<"deg.\n";
  angular_resolution = pcl::deg2rad (angular_resolution);
  
  // ------------------------------------------------------------------
  // -----Read pcd file or create example point cloud if not given-----
  // ------------------------------------------------------------------
  pcl::PointCloud<PointType>::Ptr point_cloud_ptr (new pcl::PointCloud<PointType>);
  pcl::PointCloud<PointType>& point_cloud = *point_cloud_ptr;
  pcl::PointCloud<pcl::PointWithViewpoint> far_ranges;
  Eigen::Affine3f scene_sensor_pose (Eigen::Affine3f::Identity ());
  std::vector<int> pcd_filename_indices = pcl::console::parse_file_extension_argument (argc, argv, "pcd");
  if (!pcd_filename_indices.empty ())
  {
    std::string filename = argv[pcd_filename_indices[0]];
    if (pcl::io::loadPCDFile (filename, point_cloud) == -1)
    {
      std::cout << "Was not able to open file \""<<filename<<"\".\n";
      printUsage (argv[0]);
      return 0;
    }
    scene_sensor_pose = Eigen::Affine3f (Eigen::Translation3f (point_cloud.sensor_origin_[0],
                                                               point_cloud.sensor_origin_[1],
                                                               point_cloud.sensor_origin_[2])) *
                        Eigen::Affine3f (point_cloud.sensor_orientation_);
  
    std::string far_ranges_filename = pcl::getFilenameWithoutExtension (filename)+"_far_ranges.pcd";
    if (pcl::io::loadPCDFile(far_ranges_filename.c_str(), far_ranges) == -1)
      std::cout << "Far ranges file \""<<far_ranges_filename<<"\" does not exists.\n";
  }
  else
  {
    std::cout << "\nNo *.pcd file given => Generating example point cloud.\n\n";
    for (float x=-0.5f; x<=0.5f; x+=0.01f)
    {
      for (float y=-0.5f; y<=0.5f; y+=0.01f)
      {
        PointType point;  point.x = x;  point.y = y;  point.z = 2.0f - y;
        point_cloud.points.push_back (point);
      }
    }
    point_cloud.width = (int) point_cloud.points.size ();  point_cloud.height = 1;
  }
  
  // -----------------------------------------------
  // -----Create RangeImage from the PointCloud-----
  // -----------------------------------------------
  float noise_level = 0.0;
  float min_range = 0.0f;
  int border_size = 1;
  pcl::RangeImage::Ptr range_image_ptr (new pcl::RangeImage);
  pcl::RangeImage& range_image = *range_image_ptr;   
  range_image.createFromPointCloud (point_cloud, angular_resolution, pcl::deg2rad (360.0f), pcl::deg2rad (180.0f),
                                   scene_sensor_pose, coordinate_frame, noise_level, min_range, border_size);
  range_image.integrateFarRanges (far_ranges);
  if (setUnseenToMaxRange)
    range_image.setUnseenToMaxRange ();

  // --------------------------------------------
  // -----Open 3D viewer and add point cloud-----
  // --------------------------------------------
  pcl::visualization::PCLVisualizer viewer ("3D Viewer");
  viewer.setBackgroundColor (1, 1, 1);
  viewer.addCoordinateSystem (1.0f, "global");
  pcl::visualization::PointCloudColorHandlerCustom<PointType> point_cloud_color_handler (point_cloud_ptr, 0, 0, 0);
  viewer.addPointCloud (point_cloud_ptr, point_cloud_color_handler, "original point cloud");
  //PointCloudColorHandlerCustom range_image_color_handler (range_image_ptr, 150, 150, 150);
  //viewer.addPointCloud (range_image_ptr, range_image_color_handler, "range image");
  //viewer.setPointCloudRenderingProperties (PCL_VISUALIZER_POINT_SIZE, 2, "range image");
  
  // -------------------------
  // -----Extract borders-----
  // -------------------------
  pcl::RangeImageBorderExtractor border_extractor (&range_image);
  pcl::PointCloud<pcl::BorderDescription> border_descriptions;
  border_extractor.compute (border_descriptions);
  
  // ----------------------------------
  // -----Show points in 3D viewer-----
  // ----------------------------------
  pcl::PointCloud<pcl::PointWithRange>::Ptr border_points_ptr(new pcl::PointCloud<pcl::PointWithRange>),
                                            veil_points_ptr(new pcl::PointCloud<pcl::PointWithRange>),
                                            shadow_points_ptr(new pcl::PointCloud<pcl::PointWithRange>);
  pcl::PointCloud<pcl::PointWithRange>& border_points = *border_points_ptr,
                                      & veil_points = * veil_points_ptr,
                                      & shadow_points = *shadow_points_ptr;
  for (int y=0; y< (int)range_image.height; ++y)
  {
    for (int x=0; x< (int)range_image.width; ++x)
    {
      if (border_descriptions.points[y*range_image.width + x].traits[pcl::BORDER_TRAIT__OBSTACLE_BORDER])
        border_points.points.push_back (range_image.points[y*range_image.width + x]);
      if (border_descriptions.points[y*range_image.width + x].traits[pcl::BORDER_TRAIT__VEIL_POINT])
        veil_points.points.push_back (range_image.points[y*range_image.width + x]);
      if (border_descriptions.points[y*range_image.width + x].traits[pcl::BORDER_TRAIT__SHADOW_BORDER])
        shadow_points.points.push_back (range_image.points[y*range_image.width + x]);
    }
  }
  pcl::visualization::PointCloudColorHandlerCustom<pcl::PointWithRange> border_points_color_handler (border_points_ptr, 0, 255, 0);
  viewer.addPointCloud<pcl::PointWithRange> (border_points_ptr, border_points_color_handler, "border points");
  viewer.setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 7, "border points");
  pcl::visualization::PointCloudColorHandlerCustom<pcl::PointWithRange> veil_points_color_handler (veil_points_ptr, 255, 0, 0);
  viewer.addPointCloud<pcl::PointWithRange> (veil_points_ptr, veil_points_color_handler, "veil points");
  viewer.setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 7, "veil points");
  pcl::visualization::PointCloudColorHandlerCustom<pcl::PointWithRange> shadow_points_color_handler (shadow_points_ptr, 0, 255, 255);
  viewer.addPointCloud<pcl::PointWithRange> (shadow_points_ptr, shadow_points_color_handler, "shadow points");
  viewer.setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 7, "shadow points");
  
  //-------------------------------------
  // -----Show points on range image-----
  // ------------------------------------
  pcl::visualization::RangeImageVisualizer* range_image_borders_widget = NULL;
  range_image_borders_widget =
    pcl::visualization::RangeImageVisualizer::getRangeImageBordersWidget (range_image, -std::numeric_limits<float>::infinity (), std::numeric_limits<float>::infinity (), false,
                                                                          border_descriptions, "Range image with borders");
  // -------------------------------------
  
  
  //--------------------
  // -----Main loop-----
  //--------------------
  while (!viewer.wasStopped ())
  {
    range_image_borders_widget->spinOnce ();
    viewer.spinOnce ();
    pcl_sleep(0.01);
  }
}

pcl的3d点云识别

#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 

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);

  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);
}

点云识别的训练和测试

#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 

int
main (int argc, char** argv)
{
  if (argc == 0 || argc % 2 == 0)
    return (-1);

  unsigned int number_of_training_clouds = (argc - 3) / 2;

  pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> normal_estimator;
  normal_estimator.setRadiusSearch (25.0);

  std::vector<pcl::PointCloud<pcl::PointXYZ>::Ptr> training_clouds;
  std::vector<pcl::PointCloud<pcl::Normal>::Ptr> training_normals;
  std::vector<unsigned int> training_classes;

  for (unsigned int i_cloud = 0; i_cloud < number_of_training_clouds - 1; i_cloud++)
  {
    pcl::PointCloud<pcl::PointXYZ>::Ptr tr_cloud(new pcl::PointCloud<pcl::PointXYZ> ());
    if ( pcl::io::loadPCDFile <pcl::PointXYZ> (argv[i_cloud * 2 + 1], *tr_cloud) == -1 )
      return (-1);

    pcl::PointCloud<pcl::Normal>::Ptr tr_normals = (new pcl::PointCloud<pcl::Normal>)->makeShared ();
    normal_estimator.setInputCloud (tr_cloud);
    normal_estimator.compute (*tr_normals);

    unsigned int tr_class = static_cast<unsigned int> (strtol (argv[i_cloud * 2 + 2], 0, 10));

    training_clouds.push_back (tr_cloud);
    training_normals.push_back (tr_normals);
    training_classes.push_back (tr_class);
  }

  pcl::FPFHEstimation<pcl::PointXYZ, pcl::Normal, pcl::Histogram<153> >::Ptr fpfh
    (new pcl::FPFHEstimation<pcl::PointXYZ, pcl::Normal, pcl::Histogram<153> >);
  fpfh->setRadiusSearch (30.0);
  pcl::Feature< pcl::PointXYZ, pcl::Histogram<153> >::Ptr feature_estimator(fpfh);

  pcl::ism::ImplicitShapeModelEstimation<153, pcl::PointXYZ, pcl::Normal> ism;
  ism.setFeatureEstimator(feature_estimator);
  ism.setTrainingClouds (training_clouds);
  ism.setTrainingNormals (training_normals);
  ism.setTrainingClasses (training_classes);
  ism.setSamplingSize (2.0f);

  pcl::ism::ImplicitShapeModelEstimation<153, pcl::PointXYZ, pcl::Normal>::ISMModelPtr model (new pcl::features::ISMModel);
  ism.trainISM (model);

  std::string file ("trained_ism_model.txt");
  model->saveModelToFile (file);

  model->loadModelFromfile (file);

  unsigned int testing_class = static_cast<unsigned int> (strtol (argv[argc - 1], 0, 10));
  pcl::PointCloud<pcl::PointXYZ>::Ptr testing_cloud (new pcl::PointCloud<pcl::PointXYZ> ());
  if ( pcl::io::loadPCDFile <pcl::PointXYZ> (argv[argc - 2], *testing_cloud) == -1 )
    return (-1);

  pcl::PointCloud<pcl::Normal>::Ptr testing_normals = (new pcl::PointCloud<pcl::Normal>)->makeShared ();
  normal_estimator.setInputCloud (testing_cloud);
  normal_estimator.compute (*testing_normals);

  pcl::features::ISMVoteList<pcl::PointXYZ>::Ptr vote_list = ism.findObjects (
    model,
    testing_cloud,
    testing_normals,
    testing_class);

  double radius = model->sigmas_[testing_class] * 10.0;
  double sigma = model->sigmas_[testing_class];
  std::vector<pcl::ISMPeak, Eigen::aligned_allocator<pcl::ISMPeak> > strongest_peaks;
  vote_list->findStrongestPeaks (strongest_peaks, testing_class, radius, sigma);

  pcl::PointCloud <pcl::PointXYZRGB>::Ptr colored_cloud = (new pcl::PointCloud<pcl::PointXYZRGB>)->makeShared ();
  colored_cloud->height = 0;
  colored_cloud->width = 1;

  pcl::PointXYZRGB point;
  point.r = 255;
  point.g = 255;
  point.b = 255;

  for (std::size_t i_point = 0; i_point < testing_cloud->points.size (); i_point++)
  {
    point.x = testing_cloud->points[i_point].x;
    point.y = testing_cloud->points[i_point].y;
    point.z = testing_cloud->points[i_point].z;
    colored_cloud->points.push_back (point);
  }
  colored_cloud->height += testing_cloud->points.size ();

  point.r = 255;
  point.g = 0;
  point.b = 0;
  for (std::size_t i_vote = 0; i_vote < strongest_peaks.size (); i_vote++)
  {
    point.x = strongest_peaks[i_vote].x;
    point.y = strongest_peaks[i_vote].y;
    point.z = strongest_peaks[i_vote].z;
    colored_cloud->points.push_back (point);
  }
  colored_cloud->height += strongest_peaks.size ();

  pcl::visualization::CloudViewer viewer ("Result viewer");
  viewer.showCloud (colored_cloud);
  while (!viewer.wasStopped ())
  {
  }

  return (0);
}

用于3D对象识别的假设验证

#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include  
#include 

typedef pcl::PointXYZRGBA PointType;
typedef pcl::Normal NormalType;
typedef pcl::ReferenceFrame RFType;
typedef pcl::SHOT352 DescriptorType;

struct CloudStyle
{
    double r;
    double g;
    double b;
    double size;

    CloudStyle (double r,
                double g,
                double b,
                double size) :
        r (r),
        g (g),
        b (b),
        size (size)
    {
    }
};

CloudStyle style_white (255.0, 255.0, 255.0, 4.0);
CloudStyle style_red (255.0, 0.0, 0.0, 3.0);
CloudStyle style_green (0.0, 255.0, 0.0, 5.0);
CloudStyle style_cyan (93.0, 200.0, 217.0, 4.0);
CloudStyle style_violet (255.0, 0.0, 255.0, 8.0);

std::string model_filename_;
std::string scene_filename_;

//Algorithm params 
bool show_keypoints_ (false);
bool use_hough_ (true);
float model_ss_ (0.02f);
float scene_ss_ (0.02f);
float rf_rad_ (0.015f);
float descr_rad_ (0.02f);
float cg_size_ (0.01f);
float cg_thresh_ (5.0f);
int icp_max_iter_ (5);
float icp_corr_distance_ (0.005f);
float hv_resolution_ (0.005f);
float hv_occupancy_grid_resolution_ (0.01f);
float hv_clutter_reg_ (5.0f);
float hv_inlier_th_ (0.005f);
float hv_occlusion_th_ (0.01f);
float hv_rad_clutter_ (0.03f);
float hv_regularizer_ (3.0f);
float hv_rad_normals_ (0.05);
bool hv_detect_clutter_ (true);

/**
 * Prints out Help message
 * @param filename Runnable App Name
 */
void
showHelp (char *filename)
{
  std::cout << std::endl;
  std::cout << "***************************************************************************" << std::endl;
  std::cout << "*                                                                         *" << std::endl;
  std::cout << "*          Global Hypothese Verification 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 keypoints." << std::endl;
  std::cout << "     --algorithm (Hough|GC):      Clustering algorithm used (default Hough)." << std::endl;
  std::cout << "     --model_ss val:              Model uniform sampling radius (default " << model_ss_ << ")" << std::endl;
  std::cout << "     --scene_ss val:              Scene uniform sampling radius (default " << scene_ss_ << ")" << std::endl;
  std::cout << "     --rf_rad val:                Reference frame radius (default " << rf_rad_ << ")" << std::endl;
  std::cout << "     --descr_rad val:             Descriptor radius (default " << descr_rad_ << ")" << std::endl;
  std::cout << "     --cg_size val:               Cluster size (default " << cg_size_ << ")" << std::endl;
  std::cout << "     --cg_thresh val:             Clustering threshold (default " << cg_thresh_ << ")" << std::endl << std::endl;
  std::cout << "     --icp_max_iter val:          ICP max iterations number (default " << icp_max_iter_ << ")" << std::endl;
  std::cout << "     --icp_corr_distance val:     ICP correspondence distance (default " << icp_corr_distance_ << ")" << std::endl << std::endl;
  std::cout << "     --hv_clutter_reg val:        Clutter Regularizer (default " << hv_clutter_reg_ << ")" << std::endl;
  std::cout << "     --hv_inlier_th val:          Inlier threshold (default " << hv_inlier_th_ << ")" << std::endl;
  std::cout << "     --hv_occlusion_th val:       Occlusion threshold (default " << hv_occlusion_th_ << ")" << std::endl;
  std::cout << "     --hv_rad_clutter val:        Clutter radius (default " << hv_rad_clutter_ << ")" << std::endl;
  std::cout << "     --hv_regularizer val:        Regularizer value (default " << hv_regularizer_ << ")" << std::endl;
  std::cout << "     --hv_rad_normals val:        Normals radius (default " << hv_rad_normals_ << ")" << std::endl;
  std::cout << "     --hv_detect_clutter val:     TRUE if clutter detect enabled (default " << hv_detect_clutter_ << ")" << std::endl << std::endl;
}

/**
 * Parses Command Line Arguments (Argc,Argv)
 * @param argc
 * @param argv
 */
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;
  }

  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_);
  pcl::console::parse_argument (argc, argv, "--icp_max_iter", icp_max_iter_);
  pcl::console::parse_argument (argc, argv, "--icp_corr_distance", icp_corr_distance_);
  pcl::console::parse_argument (argc, argv, "--hv_clutter_reg", hv_clutter_reg_);
  pcl::console::parse_argument (argc, argv, "--hv_inlier_th", hv_inlier_th_);
  pcl::console::parse_argument (argc, argv, "--hv_occlusion_th", hv_occlusion_th_);
  pcl::console::parse_argument (argc, argv, "--hv_rad_clutter", hv_rad_clutter_);
  pcl::console::parse_argument (argc, argv, "--hv_regularizer", hv_regularizer_);
  pcl::console::parse_argument (argc, argv, "--hv_rad_normals", hv_rad_normals_);
  pcl::console::parse_argument (argc, argv, "--hv_detect_clutter", hv_detect_clutter_);
}

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);
  }

  /**
   * 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);
  std::vector<int> model_good_keypoints_indices;
  std::vector<int> scene_good_keypoints_indices;

  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)
    {
      pcl::Correspondence corr (neigh_indices[0], static_cast<int> (i), neigh_sqr_dists[0]);
      model_scene_corrs->push_back (corr);
      model_good_keypoints_indices.push_back (corr.index_query);
      scene_good_keypoints_indices.push_back (corr.index_match);
    }
  }
  pcl::PointCloud<PointType>::Ptr model_good_kp (new pcl::PointCloud<PointType> ());
  pcl::PointCloud<PointType>::Ptr scene_good_kp (new pcl::PointCloud<PointType> ());
  pcl::copyPointCloud (*model_keypoints, model_good_keypoints_indices, *model_good_kp);
  pcl::copyPointCloud (*scene_keypoints, scene_good_keypoints_indices, *scene_good_kp);

  std::cout << "Correspondences found: " << model_scene_corrs->size () << std::endl;

  /**
   *  Clustering
   */
  std::vector<Eigen::Matrix4f, Eigen::aligned_allocator<Eigen::Matrix4f> > rototranslations;
  std::vector < pcl::Correspondences > clustered_corrs;

  if (use_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.recognize (rototranslations, clustered_corrs);
  }
  else
  {
    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.recognize (rototranslations, clustered_corrs);
  }

  /**
   * Stop if no instances
   */
  if (rototranslations.size () <= 0)
  {
    std::cout << "*** No instances found! ***" << std::endl;
    return (0);
  }
  else
  {
    std::cout << "Recognized Instances: " << rototranslations.size () << std::endl << std::endl;
  }

  /**
   * Generates clouds for each instances found 
   */
  std::vector<pcl::PointCloud<PointType>::ConstPtr> instances;

  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]);
    instances.push_back (rotated_model);
  }

  /**
   * ICP
   */
  std::vector<pcl::PointCloud<PointType>::ConstPtr> registered_instances;
  if (true)
  {
    std::cout << "--- ICP ---------" << std::endl;

    for (std::size_t i = 0; i < rototranslations.size (); ++i)
    {
      pcl::IterativeClosestPoint<PointType, PointType> icp;
      icp.setMaximumIterations (icp_max_iter_);
      icp.setMaxCorrespondenceDistance (icp_corr_distance_);
      icp.setInputTarget (scene);
      icp.setInputSource (instances[i]);
      pcl::PointCloud<PointType>::Ptr registered (new pcl::PointCloud<PointType>);
      icp.align (*registered);
      registered_instances.push_back (registered);
      std::cout << "Instance " << i << " ";
      if (icp.hasConverged ())
      {
        std::cout << "Aligned!" << std::endl;
      }
      else
      {
        std::cout << "Not Aligned!" << std::endl;
      }
    }

    std::cout << "-----------------" << std::endl << std::endl;
  }

  /**
   * Hypothesis Verification
   */
  std::cout << "--- Hypotheses Verification ---" << std::endl;
  std::vector<bool> hypotheses_mask;  // Mask Vector to identify positive hypotheses

  pcl::GlobalHypothesesVerification<PointType, PointType> GoHv;

  GoHv.setSceneCloud (scene);  // Scene Cloud
  GoHv.addModels (registered_instances, true);  //Models to verify
  GoHv.setResolution (hv_resolution_);
  GoHv.setResolutionOccupancyGrid (hv_occupancy_grid_resolution_);
  GoHv.setInlierThreshold (hv_inlier_th_);
  GoHv.setOcclusionThreshold (hv_occlusion_th_);
  GoHv.setRegularizer (hv_regularizer_);
  GoHv.setRadiusClutter (hv_rad_clutter_);
  GoHv.setClutterRegularizer (hv_clutter_reg_);
  GoHv.setDetectClutter (hv_detect_clutter_);
  GoHv.setRadiusNormals (hv_rad_normals_);

  GoHv.verify ();
  GoHv.getMask (hypotheses_mask);  // i-element TRUE if hvModels[i] verifies hypotheses

  for (int i = 0; i < hypotheses_mask.size (); i++)
  {
    if (hypotheses_mask[i])
    {
      std::cout << "Instance " << i << " is GOOD! <---" << std::endl;
    }
    else
    {
      std::cout << "Instance " << i << " is bad!" << std::endl;
    }
  }
  std::cout << "-------------------------------" << std::endl;

  /**
   *  Visualization
   */
  pcl::visualization::PCLVisualizer viewer ("Hypotheses Verification");
  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> ());

  pcl::PointCloud<PointType>::Ptr off_model_good_kp (new pcl::PointCloud<PointType> ());
  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::transformPointCloud (*model_good_kp, *off_model_good_kp, Eigen::Vector3f (-1, 0, 0), Eigen::Quaternionf (1, 0, 0, 0));

  if (show_keypoints_)
  {
    CloudStyle modelStyle = style_white;
    pcl::visualization::PointCloudColorHandlerCustom<PointType> off_scene_model_color_handler (off_scene_model, modelStyle.r, modelStyle.g, modelStyle.b);
    viewer.addPointCloud (off_scene_model, off_scene_model_color_handler, "off_scene_model");
    viewer.setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, modelStyle.size, "off_scene_model");
  }

  if (show_keypoints_)
  {
    CloudStyle goodKeypointStyle = style_violet;
    pcl::visualization::PointCloudColorHandlerCustom<PointType> model_good_keypoints_color_handler (off_model_good_kp, goodKeypointStyle.r, goodKeypointStyle.g,
                                                                                                    goodKeypointStyle.b);
    viewer.addPointCloud (off_model_good_kp, model_good_keypoints_color_handler, "model_good_keypoints");
    viewer.setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, goodKeypointStyle.size, "model_good_keypoints");

    pcl::visualization::PointCloudColorHandlerCustom<PointType> scene_good_keypoints_color_handler (scene_good_kp, goodKeypointStyle.r, goodKeypointStyle.g,
                                                                                                    goodKeypointStyle.b);
    viewer.addPointCloud (scene_good_kp, scene_good_keypoints_color_handler, "scene_good_keypoints");
    viewer.setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, goodKeypointStyle.size, "scene_good_keypoints");
  }

  for (std::size_t i = 0; i < instances.size (); ++i)
  {
    std::stringstream ss_instance;
    ss_instance << "instance_" << i;

    CloudStyle clusterStyle = style_red;
    pcl::visualization::PointCloudColorHandlerCustom<PointType> instance_color_handler (instances[i], clusterStyle.r, clusterStyle.g, clusterStyle.b);
    viewer.addPointCloud (instances[i], instance_color_handler, ss_instance.str ());
    viewer.setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, clusterStyle.size, ss_instance.str ());

    CloudStyle registeredStyles = hypotheses_mask[i] ? style_green : style_cyan;
    ss_instance << "_registered" << std::endl;
    pcl::visualization::PointCloudColorHandlerCustom<PointType> registered_instance_color_handler (registered_instances[i], registeredStyles.r,
                                                                                                   registeredStyles.g, registeredStyles.b);
    viewer.addPointCloud (registered_instances[i], registered_instance_color_handler, ss_instance.str ());
    viewer.setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, registeredStyles.size, ss_instance.str ());
  }

  while (!viewer.wasStopped ())
  {
    viewer.spinOnce ();
  }

  return (0);
}

pcl库的里东西还是有点多,感觉这些也没啥意思,还是有个而明确的目标,然后一个小的目标目标去实现,我觉得这样可能对我更好好一些,
然后我在这里pcl 官网的链接贴出来,http://www.pointclouds.org/documentation/tutorials/