PCL库常用算法

PCL（ Point Cloud Library）是用于处理2D/3D 图像以及点云的一个大型开源项目。学习PCL最好的途径是阅读其官网文档（Point Cloud Library (PCL)）。虽然PCL的网站文档稍微有点“丑”，但是其内容十分详尽。从应用的角度而言，PCL可以用于点云的分割、分类、校准以及可视化等方面。从理论角度而言，PCL中包含的众多算法能更好得帮助人们理解与创造新的点云算法。无论是工业应用还是科研攻关，PCL都能在三维数据处理领域祝您一臂之力。

激光雷达作为自动驾驶最常用的传感器，经常需要使用激光雷达来做建图、定位和感知等任务。

而这时候使用降低点云规模的预处理方法，可以能够去除无关区域的点以及降低点云规模。并能够给后续的PCL点云分割带来有效的收益。

1. 特征提取

1.1. 三维激光雷达压缩成二维

void filterGroundPlane(const PCLPointCloud& pc, PCLPointCloud& ground, PCLPointCloud& nonground) const{  ground.header = pc.header;  nonground.header = pc.header;  if (pc.size() < 50){    ROS_WARN("Pointcloud in OctomapServer too small, skipping ground plane extraction");    nonground = pc;  } else {      // https://blog.csdn.net/weixin_41552975/article/details/120428619    // 指模型参数，如果是平面的话应该是指a b c d四个参数值    pcl::ModelCoefficients::Ptr coefficients (new pcl::ModelCoefficients);    pcl::PointIndices::Ptr inliers (new pcl::PointIndices);    // 创建分割对象    pcl::SACSegmentation<PCLPoint> seg;    //可选设置    seg.setOptimizeCoefficients (true);    //必须设置    seg.setModelType(pcl::SACMODEL_PERPENDICULAR_PLANE);    seg.setMethodType(pcl::SAC_RANSAC);    // 设置迭代次数的上限    seg.setMaxIterations(200);    // 设置距离阈值    seg.setDistanceThreshold (0.04);    //设置所搜索平面垂直的轴     seg.setAxis(Eigen::Vector3f(0,0,1));    //设置待检测的平面模型和上述轴的最大角度    seg.setEpsAngle(0.15);    // pc 赋值    PCLPointCloud cloud_filtered(pc);    //创建滤波器    pcl::ExtractIndices<PCLPoint> extract;    bool groundPlaneFound = false;    while(cloud_filtered.size() > 10 && !groundPlaneFound){         // 所有点云传入，并通过coefficients提取到所有平面      seg.setInputCloud(cloud_filtered.makeShared());      seg.segment (*inliers, *coefficients);      if (inliers->indices.size () == 0){        ROS_INFO("PCL segmentation did not find any plane.");        break;      }      //输入要滤波的点云      extract.setInputCloud(cloud_filtered.makeShared());      //被提取的点的索引集合      extract.setIndices(inliers);      if (std::abs(coefficients->values.at(3)) < 0.07){        ROS_DEBUG("Ground plane found: %zu/%zu inliers. Coeff: %f %f %f %f", inliers->indices.size(), cloud_filtered.size(),                  coefficients->values.at(0), coefficients->values.at(1), coefficients->values.at(2), coefficients->values.at(3));        //true:滤波结果取反，false，则是取正        extract.setNegative (false);        //获取地面点集合，并传入ground        extract.filter (ground);        // 存在有不是平面的点        if(inliers->indices.size() != cloud_filtered.size()){          extract.setNegative(true);          PCLPointCloud cloud_out;          // 传入cloud_out          extract.filter(cloud_out);          // 不断减少cloud_filtered数目，同时累加nonground数目          cloud_filtered = cloud_out;          nonground += cloud_out;        }        groundPlaneFound = true;      } else{ // 否则提取那些不是平面的，然后剩下的就是平面点        ROS_DEBUG("Horizontal plane (not ground) found: %zu/%zu inliers. Coeff: %f %f %f %f", inliers->indices.size(), cloud_filtered.size(),                  coefficients->values.at(0), coefficients->values.at(1), coefficients->values.at(2), coefficients->values.at(3));        pcl::PointCloud<PCLPoint> cloud_out;        extract.setNegative (false);        extract.filter(cloud_out);        nonground +=cloud_out;        if(inliers->indices.size() != cloud_filtered.size()){          extract.setNegative(true);          cloud_out.points.clear();          extract.filter(cloud_out);          cloud_filtered = cloud_out;        } else{          cloud_filtered.points.clear();        }      }    }    // 由于没有找到平面，则会进入下面    if (!groundPlaneFound){      ROS_WARN("No ground plane found in scan");      // 对高度进行粗略调整，以防止出现虚假障碍物      pcl::PassThrough<PCLPoint> second_pass;      second_pass.setFilterFieldName("z");      second_pass.setFilterLimits(-m_groundFilterPlaneDistance, m_groundFilterPlaneDistance);      second_pass.setInputCloud(pc.makeShared());      second_pass.filter(ground);      second_pass.setFilterLimitsNegative (true);      second_pass.filter(nonground);    }    // Create a set of planar coefficients with X=Y=0,Z=1    pcl::ModelCoefficients::Ptr coefficients1(new pcl::ModelCoefficients());    coefficients1->values.resize(4);    coefficients1->values[0] = 1;    coefficients1->values[1] = 0;    coefficients1->values[2] = 0;    coefficients1->values[3] = 0;    // Create the filtering object    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_projected(new pcl::PointCloud<pcl::PointXYZ>);    pcl::ProjectInliers<pcl::PointXYZ> proj;    proj.setModelType(pcl::SACMODEL_PLANE);    proj.setInputCloud(nonground);    proj.setModelCoefficients(coefficients1);    proj.filter(*cloud_projected);    if (cloud_projected.size() > 0)             writer.write<PCLPoint>("cloud_projected.pcd",cloud_projected, false);  }}

1.2. 面特征提取

PCL中Sample——consensus模块提供了RANSAC平面拟合模块。

SACMODEL_PLANE 模型：定义为平面模型，共设置四个参数 [normal_x，normal_y，normal_z，d]。其中，（normal_x，normal_y，normal_z）为平面法向量，d为常数项。

pcl::SACSegmentationFromNormals seg;
 
//创建分割时所需要的模型系数对象，coefficients及存储内点的点索引集合对象inliers 
pcl::ModelCoefficients::Ptr coefficients(new pcl::ModelCoefficients); 
pcl::PointIndices::Ptr inliers(new pcl::PointIndices); 
// 创建分割对象 
pcl::SACSegmentation& lt;
pcl::PointXYZ& gt;
// 可选择配置，设置模型系数需要优化
seg.setOptimizeCoefficients(true); 
// 必要的配置，设置分割的模型类型，所用的随机参数估计方法，距离阀值，输入点云 
seg.setModelType(pcl::SACMODEL_PLANE); //设置模型类型 
seg.setMethodType(pcl::SAC_RANSAC);
//设置随机采样一致性方法类型 
seg.setDistanceThreshold(0.01);
//设定距离阀值，距离阀值决定了点被认为是局内点是必须满足的条件国，表示点到估计模型的距离最大值
seg.setInputCloud(cloud);
//引发分割实现，存储分割结果到点几何inliers及存储平面模型的系数coefficients 
seg.segment(*inliers, *coefficients);

1.3. 圆柱体提取

圆柱体的提取也是基于Ransec来实现提取，RANSAC从样本中随机抽选出一个样本子集，使用最小方差估计算法对这个子集计算模型参数，然后计算所有样本与该模型的偏差。

再使用一个预先设定好的阈值与偏差比较，当偏差小于阈值时，该样本点属于模型内样本点（inliers），简称内点，否则为模型外样本点（outliers），简称外点。

pcl::SACSegmentationFromNormals seg;
 
// Create the segmentation object for cylinder segmentation and set all the parameters
seg.setOptimizeCoefficients(true);
seg.setModelType(pcl::SACMODEL_CYLINDER);   // 提取圆柱体的操作
seg.setMethodType(pcl::SAC_RANSAC);
seg.setNormalDistanceWeight(0.1);
seg.setMaxIterations(10000);
seg.setDistanceThreshold(0.05);   // 距离5cm
seg.setRadiusLimits(0, 0.1);    // 半径 10cm
seg.setInputCloud(cloud_filtered2);
seg.setInputNormals(cloud_normals2);
 
// Obtain the cylinder inliers and coefficients
seg.segment(*inliers_cylinder, *coefficients_cylinder);
std::cerr << "Cylinder coefficients: " << *coefficients_cylinder << std::endl;

1.4. 半径近邻

半径内近邻搜索（Neighbors within Radius Search），是指搜索点云中一点在球体半径 R内的所有近邻点。

// Neighbors within radius search
std::vector<int> pointIdxRadiusSearch;
std::vector<float> pointRadiusSquaredDistance;
float radius = 256.0f * rand () / (RAND_MAX + 1.0f);
 
if ( kdtree.radiusSearch (searchPoint, radius, pointIdxRadiusSearch, pointRadiusSquaredDistance) > 0 )
{
  for (size_t i = 0; i < pointIdxRadiusSearch.size (); ++i)
    std::cout << "    "  <<   cloud->points[ pointIdxRadiusSearch[i] ].x 
              << " " << cloud->points[ pointIdxRadiusSearch[i] ].y 
              << " " << cloud->points[ pointIdxRadiusSearch[i] ].z 
              << " (squared distance: " << pointRadiusSquaredDistance[i] << ")" << std::endl;
}

1.5. 聚类

首先选取种子点，利用kd-tree对种子点进行半径r邻域搜索，若邻域内存在点，则与种子点归为同一聚类簇Q；

 
欧式聚类：
void Cvisualization::ShowCloud4()
{
    //读入点云数据table_scene_lms400.pcd
    pcl::PCDReader reader;
    pcl::PointCloud::Ptr cloud (new pcl::PointCloud), cloud_f (new pcl::PointCloud);
    reader.read ("E:/ai/pcltest/20210903changhuAM-0001.pcd", *cloud);
    std::cout << "PointCloud before filtering has: " << cloud->points.size () << " data points." << std::endl; //*
    //    /*从输入的.PCD文件载入数据后，我们创建了一个VoxelGrid滤波器对数据进行下采样，我们在这里进行下采样的原       因是来加速处理过程，越少的点意味着分割循环中处理起来越快。*/
    // Create the filtering object: downsample the dataset using a leaf size of 1cm
    pcl::VoxelGrid vg; //体素栅格下采样对象
    pcl::PointCloud::Ptr cloud_filtered (new pcl::PointCloud);
    vg.setInputCloud (cloud);
    vg.setLeafSize (0.01f, 0.01f, 0.01f); //设置采样的体素大小
    vg.filter (*cloud_filtered);  //执行采样保存数据
    std::cout << "PointCloud after filtering has: " << cloud_filtered->points.size ()  << " data points." << std::endl; //*
 
    // Create the segmentation object for the planar model and set all the parameters
    pcl::SACSegmentation seg;//创建分割对象
    pcl::PointIndices::Ptr inliers (new pcl::PointIndices);
    pcl::ModelCoefficients::Ptr coefficients (new pcl::ModelCoefficients);
    pcl::PointCloud::Ptr cloud_plane (new pcl::PointCloud ());
    pcl::PCDWriter writer;
    seg.setOptimizeCoefficients (true);  //设置对估计的模型参数进行优化处理
    seg.setModelType (pcl::SACMODEL_PLANE);//设置分割模型类别
    seg.setMethodType (pcl::SAC_RANSAC);//设置用哪个随机参数估计方法
    seg.setMaxIterations (100);  //设置最大迭代次数
    seg.setDistanceThreshold (0.02);    //设置判断是否为模型内点的距离阈值
 
    int i=0, nr_points = (int) cloud_filtered->points.size ();
    while (cloud_filtered->points.size () > 0.3 * nr_points)
    {
        // Segment the largest planar component from the remaining cloud
        //      /*为了处理点云中包含多个模型，我们在一个循环中执行该过程，并在每次模型被提取后，我们保存剩余的点，进行迭代。模型内点通过分割过程获取，如下*/
        seg.setInputCloud (cloud_filtered);
        seg.segment (*inliers, *coefficients);
        if (inliers->indices.size () == 0)
        {
            std::cout << "Could not estimate a planar model for the given dataset." << std::endl;
            break;
        }
 
        //移去平面局内点，提取剩余点云
        pcl::ExtractIndices extract;   //创建点云提取对象
        extract.setInputCloud (cloud_filtered);    //设置输入点云
        extract.setIndices (inliers);   //设置分割后的内点为需要提取的点集
        extract.setNegative (false); //设置提取内点而非外点
        // Get the points associated with the planar surface
        extract.filter (*cloud_plane);   //提取输出存储到cloud_plane
        std::cout << "PointCloud representing the planar component: " << cloud_plane->points.size () << " data points." << std::endl;
 
        // Remove the planar inliers, extract the rest
        extract.setNegative (true);
        extract.filter (*cloud_f);
        *cloud_filtered = *cloud_f;
    }
 
    // Creating the KdTree object for the search method of the extraction
    pcl::search::KdTree::Ptr tree (new pcl::search::KdTree);
    tree->setInputCloud (cloud_filtered); //创建点云索引向量，用于存储实际的点云信息
 
    std::vector cluster_indices;
    pcl::EuclideanClusterExtraction ec;
    ec.setClusterTolerance (0.2); //设置近邻搜索的搜索半径为2cm
    ec.setMinClusterSize (100);//设置一个聚类需要的最少点数目为100
    ec.setMaxClusterSize (25000);//设置一个聚类需要的最大点数目为25000
    ec.setSearchMethod (tree);//设置点云的搜索机制
    ec.setInputCloud (cloud_filtered);
    ec.extract (cluster_indices);//从点云中提取聚类，并将点云索引保存在cluster_indices中
 
    //    /* 为了从点云索引向量中分割出每个聚类，必须迭代访问点云索引，每次创建一个新的点云数据集，并且将所有当前聚类的点写入到点云数据集中 */
    //迭代访问点云索引cluster_indices，直到分割出所有聚类
    int j = 0;
    for (std::vector::const_iterator it = cluster_indices.begin (); it != cluster_indices.end (); ++it)
    {
        pcl::PointCloud::Ptr cloud_cluster (new pcl::PointCloud);
        //创建新的点云数据集cloud_cluster，将所有当前聚类写入到点云数据集中
        for (std::vector<int>::const_iterator pit = it->indices.begin (); pit != it->indices.end (); ++pit)
            cloud_cluster->points.push_back (cloud_filtered->points[*pit]); //*
        cloud_cluster->width = cloud_cluster->points.size ();
        cloud_cluster->height = 1;
        cloud_cluster->is_dense = true;
 
        std::cout << "PointCloud representing the Cluster: " << cloud_cluster->points.size () << " data points." << std::endl;
        std::stringstream ss;
        ss << "E:/ai/pcltest/cloud_cluster_" << j << ".pcd";
        writer.write (ss.str (), *cloud_cluster, false);
        j++;
    }
 
    pcl::visualization::PCLVisualizer::Ptr viewer(new pcl::visualization::PCLVisualizer("HelloMyFirstVisualPCL"));
    viewer->addPointCloud(cloud, "sample cloud");
    while (!viewer->wasStopped())
    {
        viewer->spinOnce(100);
        boost::this_thread::sleep(boost::posix_time::microseconds(100000));
    }
}

1.6. 区域生长

区域生长的基本思想是将具有相似性质的点集合起来构成区域。

首先对每个需要分割的区域找出一个种子作为生长的起点,然后将种子周围邻域中与种子有相同或相似性质的点(根据事先确定的生长或相似准则来确定，多为法向量、曲率)归并到种子所在的区域中。

#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
 
 
int main()
{
   
    pcl::PointCloud::Ptr cloud(new pcl::PointCloud);
 
    if (pcl::io::loadPCDFile("data//table_scene_lms400.pcd", *cloud) == -1)
    {
        std::cout << "Cloud reading failed." << std::endl;
        return (-1);
    }
    // 设置搜索方式为kdTree
    pcl::search::Search::Ptr tree(new pcl::search::KdTree);
    // 计算法向量
    pcl::PointCloud ::Ptr normals(new pcl::PointCloud );
    pcl::NormalEstimation normal_estimator;
    normal_estimator.setSearchMethod(tree);
    normal_estimator.setInputCloud(cloud);
    normal_estimator.setKSearch(50);
    normal_estimator.compute(*normals);
    //直通滤波在Z轴的0到1米之间
    pcl::IndicesPtr indices(new std::vector <int>);
    pcl::PassThrough pass;
    pass.setInputCloud(cloud);
    pass.setFilterFieldName("z");
    pass.setFilterLimits(0.0, 1.0);
    pass.filter(*indices);
    // 欧式聚类
    pcl::RegionGrowing reg;
    reg.setMinClusterSize(5000);     //最小的聚类的点数
    reg.setMaxClusterSize(1000000);  //最大的聚类的点数
    reg.setSearchMethod(tree);       //搜索方式
    reg.setNumberOfNeighbours(30);   //设置搜索的邻域点的个数
    reg.setInputCloud(cloud);        //输入点
    //reg.setIndices (indices);
    reg.setInputNormals(normals);    //输入的法线
    reg.setSmoothnessThreshold(3.0 / 180.0 * M_PI);  //设置平滑度
    reg.setCurvatureThreshold(1.0);  //设置曲率的阀值
    // 获取聚类的结果，分割结果保存在点云索引的向量中
    std::vector  clusters;
    reg.extract(clusters);
    //输出聚类的数量
    std::cout << "Number of clusters is equal to " << clusters.size() << std::endl;
    // 输出第一个聚类的数量
    std::cout << "First cluster has " << clusters[0].indices.size() << " points." << endl;
    std::cout << "These are the indices of the points of the initial" <<
        std::endl << "cloud that belong to the first cluster:" << std::endl;
 
    int counter = 0;
    while (counter < clusters[0].indices.size())
    {
        std::cout << clusters[0].indices[counter] << ", ";
        counter++;
        if (counter % 10 == 0)
            std::cout << std::endl;
    }
    std::cout << std::endl;
 
    //可视化聚类的结果
    pcl::PointCloud ::Ptr colored_cloud = reg.getColoredCloud();
    pcl::visualization::CloudViewer viewer("Cluster viewer");
    viewer.showCloud(colored_cloud);
    while (!viewer.wasStopped())
    {
    }
 
    return (0);
}

1.7. 线特征拟合

一般线特征拟合的方式前提是先要滤除不必要的点，而这个就需要使用K-D tree来先实现搜索

#include 
#include 
#include 
#include 
#include 
#include 
#include 
 
using namespace std::chrono_literals;
 
pcl::visualization::PCLVisualizer::Ptr
simpleVis(pcl::PointCloud::ConstPtr cloud)
{
  // --------------------------------------------
  // -----Open 3D viewer and add point cloud-----
  // --------------------------------------------
  pcl::visualization::PCLVisualizer::Ptr viewer(
      new pcl::visualization::PCLVisualizer("3D Viewer"));
  viewer->setBackgroundColor(0, 0, 0);
  viewer->addPointCloud(cloud, "sample cloud");
  viewer->setPointCloudRenderingProperties(
      pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 3, "sample cloud");
  // viewer->addCoordinateSystem (1.0, "global");
  //viewer->initCameraParameters();
  return (viewer);
}
 
pcl::PointCloud::Ptr
create_line(double x0, double y0, double z0, double a, double b, double c, double point_size = 1000, double step = 0.1)
{
  pcl::PointCloud::Ptr cloud_line(new pcl::PointCloud);
  cloud_line->width = point_size;
  cloud_line->height = 1;
  cloud_line->resize(cloud_line->width * cloud_line->height);
 
  for (std::size_t i = 0; i < cloud_line->points.size(); ++i) {
    cloud_line->points[i].x = x0 + a / std::pow(a * a + b * b + c * c, 0.5) * i * 0.1;
    cloud_line->points[i].y = y0 + b / std::pow(a * a + b * b + c * c, 0.5) * i * 0.1;
    cloud_line->points[i].z = z0 + c / std::pow(a * a + b * b + c * c, 0.5) * i * 0.1;
  }
  return cloud_line;
}
 
void fit_line(pcl::PointCloud::Ptr& cloud, double distance_threshold)
{
  // fit line from a point cloud
  pcl::ModelCoefficients::Ptr coefficients1(new pcl::ModelCoefficients);
  pcl::PointIndices::Ptr inliers1(new pcl::PointIndices);
  pcl::SACSegmentation seg;
  seg.setOptimizeCoefficients(true);
  seg.setModelType(pcl::SACMODEL_LINE);
  seg.setMethodType(pcl::SAC_RANSAC);
  seg.setMaxIterations(1000);
  seg.setDistanceThreshold(distance_threshold);
  seg.setInputCloud(cloud);
  seg.segment(*inliers1, *coefficients1);
  // line parameters
  double x0, y0, z0, a, b, c;
 
  x0 = coefficients1->values[0];
  y0 = coefficients1->values[1];
  z0 = coefficients1->values[2];
  a = coefficients1->values[3];
  b = coefficients1->values[4];
  c = coefficients1->values[5];
  std::cout << "model parameters1:"
            << "   (x - " << x0 << ") / " << a << " = (y - " << y0 << ") / " << b
            << " = (z - " << z0 << ") / " << c << std::endl;
 
  // extract segmentation part
  pcl::PointCloud::Ptr cloud_line1(new pcl::PointCloud);
  pcl::ExtractIndices extract;
  extract.setInputCloud(cloud);
  extract.setIndices(inliers1);
  extract.setNegative(false);
  extract.filter(*cloud_line1);
 
  // extract remain pointcloud
  pcl::PointCloud::Ptr cloud_remain(new pcl::PointCloud);
  extract.setNegative(true);
  extract.filter(*cloud_remain);
 
  //显示原始点云
  pcl::visualization::PCLVisualizer::Ptr viewer_ori;
  viewer_ori = simpleVis(cloud);
  while (!viewer_ori->wasStopped()) {
    viewer_ori->spinOnce(100);
    std::this_thread::sleep_for(100ms);
  }
 
  pcl::visualization::PCLVisualizer::Ptr viewer(new pcl::visualization::PCLVisualizer("3D Viewer"));
  viewer->setBackgroundColor(0, 0, 0);
 
  viewer->addPointCloud(cloud_remain, "cloud_remain");
  viewer->setPointCloudRenderingProperties(
      pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 2, "cloud_remain");
 
  viewer->addPointCloud(cloud_line1, "cloud_line1");
  viewer->setPointCloudRenderingProperties(
      pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 5, "cloud_line1");
  viewer->setPointCloudRenderingProperties(
      pcl::visualization::PCL_VISUALIZER_COLOR, 1.0, 0.5, 0.5, "cloud_line1");
 
  while (!viewer->wasStopped()) {
    viewer->spinOnce(100);
    std::this_thread::sleep_for(100ms);
  }
}
 
void demo()
{
  // line parameters
  double x0 = -2, y0 = -2, z0 = 0, a = 1, b = 1, c = 0;
  auto line_pcd_create = create_line(x0, y0, z0, a, b, c);
  pcl::PointCloud::Ptr cloud_noise(new pcl::PointCloud);
 
  std::size_t noise_points_size = line_pcd_create->points.size() / 10;
  cloud_noise->width = noise_points_size;
  cloud_noise->height = 1;
  cloud_noise->points.resize(cloud_noise->width * cloud_noise->height);
  // add noise
  for (std::size_t i = 0; i < noise_points_size; ++i) {
    int random_num = line_pcd_create->points.size() * rand() / (RAND_MAX + 1.0f);
    cloud_noise->points[i].x =
        line_pcd_create->points[random_num].x + 10 * rand() / (RAND_MAX + 1.0f) - 5;
    cloud_noise->points[i].y =
        line_pcd_create->points[random_num].y + 10 * rand() / (RAND_MAX + 1.0f) - 5;
    cloud_noise->points[i].z =
        line_pcd_create->points[random_num].z + 10 * rand() / (RAND_MAX + 1.0f) - 5;
  }
 
  pcl::PointCloud::Ptr line_with_noise(new pcl::PointCloud);
 
  *line_with_noise = *cloud_noise + *line_pcd_create;
 
  fit_line(line_with_noise, 1);
}
 
int main(int argc, char* argv[])
{
  if (argc < 3) {
    std::cout << "please input parametars:\nfilepath\ndistance_threshold" << std::endl;
    demo();
    return -1;
  }
  std::string file_path = argv[1];
  double distance_threshold = atof(argv[2]);
 
  pcl::PointCloud::Ptr cloud(new pcl::PointCloud);
 
  if (pcl::io::loadPLYFile(file_path, *cloud) < 0) {
    std::cout << "can not read file " << file_path << std::endl;
    return -1;
  }
  std::cout << "point size: " << cloud->points.size() << std::endl;
 
  fit_line(cloud, distance_threshold);
  return 0;
}
 

1.8. 点特征提取

点特征的提取和线特征的提取原理一样

    pcl::HarrisKeypoint3D<pcl::PointXYZ, pcl::PointXYZI, pcl::Normal> harris;    harris.setInputCloud(cloud);//设置输入点云 指针    harris.setNonMaxSupression(true);    harris.setRadius(0.6f);//　块体半径    harris.setThreshold(0.01f);//数量阈值    //新建的点云必须初始化，清零，否则指针会越界    //注意Harris的输出点云必须是有强度(I)信息的 pcl::PointXYZI，因为评估值保存在I分量里    pcl::PointCloud<pcl::PointXYZI>::Ptr cloud_out_ptr(new pcl::PointCloud<pcl::PointXYZI>);    // 计算特征点    harris.compute(*cloud_out_ptr);

参考文献

自动驾驶-激光雷达预处理/特征提取

PCL入门系列一——PCL简介及PCL安装 - 知乎

pcl教程（五）聚类_紫沐衙的博客-CSDN博客