pcl--第三节关键点

简介

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。

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:

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

为了满足上述要求,提出以下探测步骤来进行关键点提取:

遍历每个深度图像点,通过寻找在近区域有深度突变的位置进行边缘检测。
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。
根据第二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。
对兴趣值进行平滑过滤。
进行无最大值压缩找到最终的关键点，即为 NARF关键点


/* \author Bastian Steder */
 
#include 
 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include  // for getFilenameWithoutExtension
 
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] \n\n"
        << "Options:\n"
        << "-------------------------------------------\n"
        << "-r    angular resolution in degrees (default " << angular_resolution << ")\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 " << 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;
        std::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);
        std::cout << "Using coordinate frame " << (int)coordinate_frame << ".\n";
    }
    if (pcl::console::parse(argc, argv, "-s", support_size) >= 0)
        std::cout << "Setting support size to " << support_size << ".\n";
    if (pcl::console::parse(argc, argv, "-r", angular_resolution) >= 0)
        std::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::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)
        {
            std::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;
        std::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.push_back(point);
            }
        }
        point_cloud.width = point_cloud.size();  point_cloud.height = 1;
    }
 
    // -----------------------------------------------
    // -----Create RangeImage from the PointCloud-----
    // -----------------------------------------------
    float noise_level = 0.0;
    float min_range = 0.0f;
    int border_size = 1;
    pcl::RangeImage::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 " << keypoint_indices.size() << " key points.\n";
 
    // ----------------------------------------------
    // -----Show keypoints in range image widget-----
    // ----------------------------------------------
    //for (std::size_t i=0; i
      //range_image_widget.markPoint (keypoint_indices[i]%range_image.width,
                                    //keypoint_indices[i]/range_image.width);
 
    // -------------------------------------
    // -----Show keypoints in 3D viewer-----
    // -------------------------------------
    pcl::PointCloud::Ptr keypoints_ptr(new pcl::PointCloud);
    pcl::PointCloud& keypoints = *keypoints_ptr;
    keypoints.resize(keypoint_indices.size());
    for (std::size_t i = 0; i < keypoint_indices.size(); ++i)
        keypoints[i].getVector3fMap() = range_image[keypoint_indices[i]].getVector3fMap();
 
    pcl::visualization::PointCloudColorHandlerCustom 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);
    }
}

找不到那个far文件，无法运行

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原文地址：https://blog.csdn.net/weixin_42398658/article/details/132854713

pcl--第三节 关键点

简介

pcl--第三节关键点