返回 >>>>>> PCL-3D点云总目录

关键点

关键点也称为兴趣点，它是 2D 图像或 3D 点云或曲面模型上,可以通过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,关键点的数量比原始点云或图像的数据量少很多，其与局部特征描述子结合组成关键点描述子。常用来构成原始数据的紧凑表示 ，具有代表性与描述性，从而加快后续识别、追踪等对数据的处理速度 。

固而，关键点提取就成为 2D 与 3D 信息处理中不可或缺的关键技术 。

关键点概念及算法

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，关键点探测的重要一步是减少特征提取时的搜索空间，把重点放在重要的结构上，对 NARF 关键点提取过程有以下要求：

讯享网

• 提取的过程必须考虑边缘以及物体表面变化信息
• 即使换了不同的视角，关键点的位置必须稳定的可以被重复探测
• 关键点所在的位置必须有稳定的支持区域，可以计算描述子和估计唯一的法向量。

为了满足上述要求，可以通过以下探测步骤来进行关键点提取：

1. 遍历每个深度图像点，通过寻找在近邻区域有深度突变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，以及变化的主方向；
3. 根据第2步找到的主方向计算兴趣值，表征该方向与其他方向的不同，以及该处表面的变化情况，即该点有多稳定；
4. 对兴趣值进行平滑过滤；
5. 进行无最大值压缩找到最终的关键点，即为 NARF 关键点。

代码实现

narf_keypoint_extraction.cpp

/* \author Bastian Steder */ #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/console/parse.h> 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] <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 as maximum range readings\n" << "-s <float> 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; 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 " << 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); // ------------------------------------------------------------------ // -----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 << "\".\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 => 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; 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, 255, 0, 0); viewer.addPointCloud(range_image_ptr, range_image_color_handler, "range image"); viewer.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 2, "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); // -------------------------------- // -----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 (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, 5, "keypoints"); //-------------------- // -----Main loop----- //-------------------- while (!viewer.wasStopped()) { range_image_widget.spinOnce(); // process GUI events viewer.spinOnce(); pcl_sleep(0.01); } } 

输出结果

讯享网Found 49 key points. 

实现效果

注：以上文字和图片均来源于链接，若有侵权请联系转载方删除。

返回 >>>>>> PCL-3D点云总目录