PCL关键点（1）

关键点也称为兴趣点，它是2D图像或是3D点云或者曲面模型上，可以通过定义检测标准来获取的具有稳定性，区别性的点集，从技术上来说，关键点的数量相比于原始点云或图像的数据量减小很多，与局部特征描述子结合在一起，组成关键点描述子常用来形成原始数据的表示，而且不失代表性和描述性，从而加快了后续的识别，追踪等对数据的处理了速度，故而，关键点技术成为在2D和3D 信息处理中非常关键的技术

 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。

 关于NARF的更为具体的描述请查看这篇博客www.cnblogs.com/ironstark/p/5051533.html。

PCL中keypoints模块及类的介绍

（1）class pcl::Keypoint<PointInT,PointOutT>  类keypoint是所有关键点检测相关类的基类，定义基本接口，具体实现由子类来完成，其继承关系时下图：

         

具体介绍：

Public Member Functions
virtual void	setSearchSurface (const PointCloudInConstPtr &cloud)
	设置搜索时所用搜索点云，cloud为指向点云对象的指针引用
void	setSearchMethod (const KdTreePtr &tree)  设置内部算法实现时所用的搜索对象，tree为指向kdtree或者octree对应的指针
void	setKSearch (int k)   设置K近邻搜索时所用的K参数
void	setRadiusSearch (double radius)   设置半径搜索的半径的参数
int	searchForNeighbors (int index, double parameter, std::vector< int > &indices, std::vector< float > &distances) const
	采用setSearchMethod设置搜索对象，以及setSearchSurface设置搜索点云，进行近邻搜索，返回近邻在点云中的索引向量， indices以及对应的距离向量distance其中为查询点的索引，parameter为搜索时所用的参数半径或者K

（2）class  pcl::HarrisKeypoint2D<PointInT,PointOutT,IntensityT>

     类HarrisKeypoint2D实现基于点云的强度字段的harris关键点检测子，其中包括多种不同的harris关键点检测算法的变种，其关键函数的说明如下：

Public Member Functions
	HarrisKeypoint2D (ResponseMethod method=HARRIS, int window_width=3, int window_height=3, int min_distance=5, float threshold=0.0)
	重构函数，method需要设置采样哪种关键点检测方法，有HARRIS，NOBLE，LOWE，WOMASI四种方法，默认为HARRIS，window_width  window_height为检测窗口的宽度和高度min_distance 为两个关键点之间 容许的最小距离，threshold为判断是否为关键点的感兴趣程度的阀值，小于该阀值的点忽略，大于则认为是关键点
void	setMethod (ResponseMethod type)设置检测方式
void	setWindowWidth (int window_width)  设置检测窗口的宽度
void	setWindowHeight (int window_height)  设置检测窗口的高度
void	setSkippedPixels (int skipped_pixels)  设置在检测时每次跳过的像素的数目
void	setMinimalDistance (int min_distance)   设置候选关键点之间的最小距离
void	setThreshold (float threshold)  设置感兴趣的阀值
void	setNonMaxSupression (bool=false)  设置是否对小于感兴趣阀值的点进行剔除，如果是true则剔除,否则返回这个点
void	setRefine (bool do_refine)设置是否对所得的关键点结果进行优化，
void	setNumberOfThreads (unsigned int nr_threads=0)  设置该算法如果采用openMP并行机制，能够创建线程数目

（3）pcl::HarrisKeypoint3D< PointInT, PointOutT, NormalT >

     类HarrisKeypoint3D和HarrisKeypoint2D类似，但是没有在点云的强度空间检测关键点，而是利用点云的3D空间的信息表面法线向量来进行关键点检测，关于HarrisKeypoint3D的类与HarrisKeypoint2D相似，除了

HarrisKeypoint3D (ResponseMethod method=HARRIS, float radius=0.01f, float threshold=0.0f) 

重构函数，method需要设置采样哪种关键点检测方法，有HARRIS，NOBLE，LOWE，WOMASI四种方法，默认为HARRIS，radius为法线估计的搜索半径，threshold为判断是否为关键点的感兴趣程度的阀值，小于该阀值的点忽略，大于则认为是关键点。

（4）pcl::HarrisKeypoint6D< PointInT, PointOutT, NormalT >

    类HarrisKeypoint6D和HarrisKeypoint2D类似，只是利用了欧式空间域XYZ或者强度域来候选关键点，或者前两者的交集，即同时满足XYZ域和强度域的关键点为候选关键点，

HarrisKeypoint6D (float radius=0.01, float threshold=0.0)  重构函数，此处并没有方法选择的参数，而是默认采用了Tomsai提出的方法实现关键点的检测，radius为法线估计的搜索半径，threshold为判断是否为关键点的感兴趣程度的阀值，小于该阀值的点忽略，大于则认为是关键点。

（5）pcl::SIFTKeypoint< PointInT, PointOutT >

    类SIFTKeypoint是将二维图像中的SIFT算子调整后移植到3D空间的SIFT算子的实现，输入带有XYZ坐标值和强度的点云，输出为点云中的SIFT关键点，其关键函数的说明如下：

void	setScales (float min_scale, int nr_octaves, int nr_scales_per_octave)
	设置搜索时与尺度相关的参数，min_scale在点云体素尺度空间中标准偏差，点云对应的体素栅格中的最小尺寸
int nr_octaves是检测关键点时体素空间尺度的数目，nr_scales_per_octave为在每一个体素空间尺度下计算高斯空间的尺度所需要的参数
void	setMinimumContrast (float min_contrast)   设置候选关键点对应的对比度下限

（6）还有很多不再一一介绍

 实例分析

实验实现提取NARF关键点，并且用图像和3D显示的方式进行可视化，可以直观的观察关键点的位置和数量 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;

// --------------------
// -----Parameters-----
// --------------------
float angular_resolution = 0.5f;           ////angular_resolution为模拟的深度传感器的角度分辨率，即深度图像中一个像素对应的角度大小
float support_size = 0.2f;                 //点云大小的设置
pcl::RangeImage::CoordinateFrame coordinate_frame = pcl::RangeImage::CAMERA_FRAME;     //设置坐标系
bool setUnseenToMaxRange = false;
bool rotation_invariant = true;

// --------------
// -----Help-----
// --------------
void 
printUsage (const char* progName)
{
  std::cout << "

Usage: "<<progName<<" [options] <scene.pcd>

"
            << "Options:
"
            << "-------------------------------------------
"
            << "-r <float>   angular resolution in degrees (default "<<angular_resolution<<")
"
            << "-c <int>     coordinate frame (default "<< (int)coordinate_frame<<")
"
            << "-m           Treat all unseen points to max range
"
            << "-s <float>   support size for the interest points (diameter of the used sphere - "
                                                                  "default "<<support_size<<")
"
            << "-o <0/1>     switch rotational invariant version of the feature on/off"
            <<               " (default "<< (int)rotation_invariant<<")
"
            << "-h           this help
"
            << "

";
}

void 
setViewerPose (pcl::visualization::PCLVisualizer& viewer, const Eigen::Affine3f& viewer_pose)  //设置视口的位姿
{
  Eigen::Vector3f pos_vector = viewer_pose * Eigen::Vector3f (0, 0, 0);   //视口的原点pos_vector
  Eigen::Vector3f look_at_vector = viewer_pose.rotation () * Eigen::Vector3f (0, 0, 1) + pos_vector;  //旋转+平移look_at_vector
  Eigen::Vector3f up_vector = viewer_pose.rotation () * Eigen::Vector3f (0, -1, 0);   //up_vector
  viewer.setCameraPosition (pos_vector[0], pos_vector[1], pos_vector[2],      //设置照相机的位姿
                            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;
    cout << "Setting unseen values in range image to maximum range readings.
";
  }
  if (pcl::console::parse (argc, argv, "-o", rotation_invariant) >= 0)
    cout << "Switching rotation invariant feature version "<< (rotation_invariant ? "on" : "off")<<".
";
  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<<".
";
  }
  if (pcl::console::parse (argc, argv, "-s", support_size) >= 0)
    cout << "Setting support size to "<<support_size<<".
";
  if (pcl::console::parse (argc, argv, "-r", angular_resolution) >= 0)
    cout << "Setting angular resolution to "<<angular_resolution<<"deg.
";
  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)
    {
      cerr << "Was not able to open file ""<<filename<<"".
";
      printUsage (argv[0]);
      return 0;
    }
    scene_sensor_pose = Eigen::Affine3f (Eigen::Translation3f (point_cloud.sensor_origin_[0], //场景传感器的位置
                                                               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.
";
  }
  else
  {
    setUnseenToMaxRange = true;
    cout << "
No *.pcd file given => Genarating example point cloud.

";
    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<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 ();
  
  // --------------------------------------------
  // -----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<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 ());
  
  // --------------------------
  // -----Show range image-----
  // --------------------------
  pcl::visualization::RangeImageVisualizer range_image_widget ("Range image");
  range_image_widget.showRangeImage (range_image);
  /*********************************************************************************************************
   创建RangeImageBorderExtractor对象，它是用来进行边缘提取的，因为NARF的第一步就是需要探测出深度图像的边缘，
   
   *********************************************************************************************************/
  // --------------------------------
  // -----Extract NARF keypoints-----
  // --------------------------------
  pcl::RangeImageBorderExtractor range_image_border_extractor;   //用来提取边缘
  pcl::NarfKeypoint narf_keypoint_detector;      //用来检测关键点
  narf_keypoint_detector.setRangeImageBorderExtractor (&range_image_border_extractor);   //
  narf_keypoint_detector.setRangeImage (&range_image);
  narf_keypoint_detector.getParameters ().support_size = support_size;    //设置NARF的参数
  
  pcl::PointCloud<int> keypoint_indices;
  narf_keypoint_detector.compute (keypoint_indices);
  std::cout << "Found "<<keypoint_indices.points.size ()<<" key points.
";

  // ----------------------------------------------
  // -----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);
  
  // -------------------------------------
  // -----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 (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");
  
  // ------------------------------------------------------
  // -----Extract NARF descriptors for interest points-----
  // ------------------------------------------------------
  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];
  pcl::NarfDescriptor narf_descriptor (&range_image, &keypoint_indices2);
  narf_descriptor.getParameters ().support_size = support_size;
  narf_descriptor.getParameters ().rotation_invariant = rotation_invariant;
  pcl::PointCloud<pcl::Narf36> narf_descriptors;
  narf_descriptor.compute (narf_descriptors);
  cout << "Extracted "<<narf_descriptors.size ()<<" descriptors for "
                      <<keypoint_indices.points.size ()<< " keypoints.
";
  
  //--------------------
  // -----Main loop-----
  //--------------------
  while (!viewer.wasStopped ())
  {
    range_image_widget.spinOnce ();  // process GUI events
    viewer.spinOnce ();
    pcl_sleep(0.01);
  }
}

运行结果：

 

未完待续**********************88888

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