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 typekeypoint_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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