PCL点云特征描述与提取（4）

如何从一个深度图像（range image）中提取NARF特征

代码解析narf_feature_extraction.cpp

#include <iostream>#include <boost/thread/thread.hpp>#include <pcl/range_image/range_image.h>#include <pcl/io/pcd_io.h>#include <pcl/visualization/range_image_visualizer.h>#include <pcl/visualization/pcl_visualizer.h>#include <pcl/features/range_image_border_extractor.h>#include <pcl/keypoints/narf_keypoint.h>#include <pcl/features/narf_descriptor.h>#include <pcl/console/parse.h>typedef pcl::PointXYZ PointType;//参数的设置float angular_resolution = 0.5f;float support_size = 0.2f;pcl::RangeImage::CoordinateFrame coordinate_frame = pcl::RangeImage::CAMERA_FRAME;bool setUnseenToMaxRange = false;bool rotation_invariant = true;//命令帮助void printUsage (const char* progName){  std::cout << "\n\nUsage: "<<progName<<" [options] <scene.pcd>\n\n"            << "Options:\n"            << "-------------------------------------------\n"            << "-r <float>   angular resolution in degrees (default "<<angular_resolution<<")\n"            << "-c <int>     coordinate frame (default "<< (int)coordinate_frame<<")\n"            << "-m           Treat all unseen points to max range\n"            << "-s <float>   support size for the interest points (diameter of the used sphere - "                                                                  "default "<<support_size<<")\n"            << "-o <0/1>     switch rotational invariant version of the feature on/off"            <<               " (default "<< (int)rotation_invariant<<")\n"            << "-h           this help\n"            << "\n\n";}void setViewerPose (pcl::visualization::PCLVisualizer& viewer, const Eigen::Affine3f& viewer_pose)//setViewerPose{  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]);}int main (int argc, char** argv){ // 设置参数检测  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;    cout << "Setting unseen values in range image to maximum range readings.\n";  }  if (pcl::console::parse (argc, argv, "-o", rotation_invariant) >= 0)    cout << "Switching rotation invariant feature version "<< (rotation_invariant ? "on" : "off")<<".\n";  int tmp_coordinate_frame;  if (pcl::console::parse (argc, argv, "-c", tmp_coordinate_frame) >= 0)  {    coordinate_frame = pcl::RangeImage::CoordinateFrame (tmp_coordinate_frame);    cout << "Using coordinate frame "<< (int)coordinate_frame<<".\n";  }  if (pcl::console::parse (argc, argv, "-s", support_size) >= 0)    cout << "Setting support size to "<<support_size<<".\n";  if (pcl::console::parse (argc, argv, "-r", angular_resolution) >= 0)    cout << "Setting angular resolution to "<<angular_resolution<<"deg.\n";  angular_resolution = pcl::deg2rad (angular_resolution);  //打开一个磁盘中的.pcd文件  但是如果没有指定就会自动生成  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 ())   //检测是否有far_ranges.pcd  {    std::string filename = argv[pcd_filename_indices[0]];    if (pcl::io::loadPCDFile (filename, point_cloud) == -1)    {      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)+"_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  {    setUnseenToMaxRange = true;    cout << "\nNo *.pcd file given => Genarating example point cloud.\n\n";    for (float x=-0.5f; x<=0.5f; x+=0.01f)   //如果没有打开的文件就生成一个矩形的点云    {      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;  }  //从点云中建立生成深度图  float noise_level = 0.0;      float min_range = 0.0f;  int border_size = 1;  boost::shared_ptr<pcl::RangeImage> 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 ();    //打开3D viewer并加入点云  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<PointType> 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 ());  //显示  pcl::visualization::RangeImageVisualizer range_image_widget ("Range image");  range_image_widget.showRangeImage (range_image);    //提取NARF特征  pcl::RangeImageBorderExtractor range_image_border_extractor;    //申明深度图边缘提取器  pcl::NarfKeypoint narf_keypoint_detector;                       //narf_keypoint_detector为点云对象  narf_keypoint_detector.setRangeImageBorderExtractor (&range_image_border_extractor);  narf_keypoint_detector.setRangeImage (&range_image);  narf_keypoint_detector.getParameters ().support_size = support_size;    //获得特征提取的大小    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 (size_t i=0; i<keypoint_indices.points.size (); ++i)    //range_image_widget.markPoint (keypoint_indices.points[i]%range_image.width,                                  //keypoint_indices.points[i]/range_image.width);    //在3Dviewer显示提取的特征信息  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 (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");    //在关键点提取NARF描述子  std::vector<int> keypoint_indices2;  keypoint_indices2.resize (keypoint_indices.points.size ());  for (unsigned int i=0; i<keypoint_indices.size (); ++i) // This step is necessary to get the right vector type    keypoint_indices2[i]=keypoint_indices.points[i];      ///建立NARF关键点的索引向量，此矢量作为NARF特征计算的输入来使用  pcl::NarfDescriptor narf_descriptor (&range_image, &keypoint_indices2);//创建narf_descriptor对象。并给了此对象输入数据（特征点索引和深度像）  narf_descriptor.getParameters ().support_size = support_size;//support_size确定计算描述子时考虑的区域大小  narf_descriptor.getParameters ().rotation_invariant = rotation_invariant;    //设置旋转不变的NARF描述子  pcl::PointCloud<pcl::Narf36> narf_descriptors;               //创建Narf36的点类型输入点云对象并进行实际计算  narf_descriptor.compute (narf_descriptors);                 //计算描述子  cout << "Extracted "<<narf_descriptors.size ()<<" descriptors for "   //打印输出特征点的数目和提取描述子的数目                      <<keypoint_indices.points.size ()<< " keypoints.\n";  //主循环函数  while (!viewer.wasStopped ())  {    range_image_widget.spinOnce ();  // process GUI events    viewer.spinOnce ();    pcl_sleep(0.01);  }}

编译运行./narf_feature_extraction -m

这将自动生成一个呈矩形的点云，检测的特征点处在角落处，参数-m是必要的，因为矩形周围的区域观测不到，但是属于边界部分，因此系统无法检测到这部分区域的特征点，选项-m将看不到的区域改变到最大范围读取，从而使系统能够使用这些边界区域。

（2）特征描述算子算法基准化分析

使用FeatureEvaluationFramework类对不同的特征描述子算法进行基准测试，基准测试框架可以测试不同种类的特征描述子算法，通过选择输入点云，算法参数，下采样叶子大小，搜索阀值等独立变量来进行测试。

使用FeatureCorrespondenceTest类执行一个单一的“基于特征的对应估计测试”执行以下的操作

   1.FeatureCorrespondenceTest类取两个输入点云（源与目标） 它将指定算法和参数，在每个点云中计算特征描述子

  2.基于n_D特征空间中的最近邻元素搜索，源点云中的每个特征将和目标点云中对应的特征相对照

  3 。对于每一个点，系统将把估计的目标点的三维位置和之前已知的实际位置相比

 4 。如果这两个点很接近（取决与决定的阀值）那么对应就成功，否则失败

 5 计算并保存成功和失败的总数，以便进一步分析

 

 

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