PCL:从深度图（pcd文件）中提取NARF关键点

 NARF(Normal Aligned Radial Feature)关键点是为了从深度图像中识别物体而提出的，对NARF关键点的提取过程有以下要求：

  a) 提取的过程考虑边缘以及物体表面变化信息在内；b)在不同视角关键点可以被重复探测；c)关键点所在位置有足够的支持区域，可以计算描述子和进行唯一的估计法向量。

  其对应的探测步骤如下：

  (1) 遍历每个深度图像点，通过寻找在近邻区域有深度变化的位置进行边缘检测。

  (2) 遍历每个深度图像点，根据近邻区域的表面变化决定一测度表面变化的系数，及变化的主方向。

  (3) 根据step(2)找到的主方向计算兴趣点，表征该方向和其他方向的不同，以及该处表面的变化情况，即该点有多稳定。

  (4) 对兴趣值进行平滑滤波。

  (5) 进行无最大值压缩找到的最终关键点，即为NARF关键点。

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

typedef pcl::PointXYZ PointType;

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

void printUsage (const char * progName)
{
  std::cout << "\n\nUsage: "<" [options] \n\n"
            << "Options:\n"
            << "-------------------------------------------\n"
            << "-r    angular resolution in degrees (default "<")\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 "<")\n"
            << "-h           this help\n"
            << "\n\n";
}


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;
      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);
      cout << "Using coordinate frame "<< (int)coordinate_frame<<".\n";
    }
    if (pcl::console::parse (argc, argv, "-s", support_size) >= 0)
      cout << "Setting support size to "<".\n";
    if (pcl::console::parse (argc, argv, "-r", angular_resolution) >= 0)
      cout << "Setting angular resolution to "<"deg.\n";
    angular_resolution = pcl::deg2rad (angular_resolution);

    // ------------------------------------------------------------------
    // -----Read pcd file or create example point cloud if not given-----
    // ------------------------------------------------------------------
    pcl::PointCloud::Ptr point_cloud_ptr (new pcl::PointCloud);
    pcl::PointCloud& point_cloud = *point_cloud_ptr;
    pcl::PointCloud 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)
      {
    cerr << "Was not able to open file \""<"\".\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 \""<"\" 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)
      {
    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;
    boost::shared_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 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 "<" key points.\n";

    // ----------------------------------------------
    // -----Show keypoints in range image widget-----
    // ----------------------------------------------
    //for (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::Ptr keypoints_ptr (new pcl::PointCloud);
    pcl::PointCloud& keypoints = *keypoints_ptr;
    keypoints.points.resize (keypoint_indices.points.size ());
    for (size_t i=0; i keypoints_color_handler (keypoints_ptr, 0, 255, 0);
    viewer.addPointCloud (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);
    }

    return 0;
}

结果如下，小伙伴们可自行尝试