16keypoints关键点

https://pcl.readthedocs.io/projects/tutorials/en/latest/#keypoints

https://pointclouds.org/documentation/group__keypoints.html

博客
http://robot.czxy.com/docs/pcl/chapter02/keypoints/
PCL关键点（1）

此模块个人代码：https://github.com/HuangCongQing/pcl-learning/tree/master/16keypoints

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介绍

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
• 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。

   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 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，

具体介绍：

（2）class pcl::HarrisKeypoint2D

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：

（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，<br />[HarrisKeypoint6D](http://docs.pointclouds.org/trunk/classpcl_1_1_harris_keypoint6_d.html#acdf288a88556dc936f8331aea5ce17ed) (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：

（6）还有很多不再一一介绍

从深度图像中提取 NARF（Normal Aligned Radial Feature）关键点

官方Tutorials：How to extract NARF keypoint from a range image

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

代码实现

创建文件：narf_keypoint_extraction.cpp
准备资源：../table_scene_lms400_downsampled.pcd
编译执行：./narf_keypoint_extraction -m ../table_scene_lms400_downsampled.pcd

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，

pcl::RangeImageBorderExtractor range_image_border_extractor; //用来提取边缘

pcl::NarfDescriptor

个人代码：16keypoints关键点/1从深度图像中提取 NARF关键点

/*
 * @Description:  提取NARF关键点，并且用图像和3D显示的方式进行可视化
 * http://robot.czxy.com/docs/pcl/chapter02/keypoints/#_2
 * https://www.cnblogs.com/li-yao7758258/p/6476359.html
 * @Author: HCQ
 * @Company(School): UCAS
 * @Email: 1756260160@qq.com
 * @Date: 2020-10-21 16:54:11
 * @LastEditTime: 2020-10-21 17:26:45
 * @FilePath: /pcl-learning/16keypoints关键点/1从深度图像中提取 NARF关键点/narf_keypoint_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> // 深度图像可视化
#include <pcl/visualization/pcl_visualizer.h>
#include <pcl/features/range_image_border_extractor.h> // 深度图像边界提取
#include <pcl/keypoints/narf_keypoint.h>               // narf_keypoint关键点检查
#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 << "\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) //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.\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);

    // ------------------------------------------------------------------
    // -----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"); // 读取pcd文件
    if (!pcd_filename_indices.empty())
    {
        std::string filename = argv[pcd_filename_indices[0]];
        if (pcl::io::loadPCDFile(filename, point_cloud) == -1)  // 不能打开文件名
        {
            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], //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.\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<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-----点云展示
    // --------------------------------------------
    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----- 深度图展示
    // --------------------------
    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.\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, 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.\n";

    //--------------------
    // -----Main loop-----
    //--------------------
    while (!viewer.wasStopped())
    {
        range_image_widget.spinOnce(); // process GUI events
        viewer.spinOnce();
        pcl_sleep(0.01);
    }
}

输出结果

运行

./narf_keypoint_extraction -m ../table_scene_lms400_downsampled.pcd

实现效果