PCL学习十：Segmentation-分割

参考引用

• Point Cloud Library
• 黑马机器人 | PCL-3D点云

PCL点云库学习笔记（文章链接汇总）

1. 引言

• 点云分割是根据空间、几何和纹理等特征对点云进行划分，使得同一划分区域内的点云拥有相似的特征。点云的有效分割往往是许多应用的前提，例如：在逆向工程 CAD/CAM 领域，对零件的不同扫描表面进行分割，然后才能更好地进行孔洞修复、曲面重建、特征描述和提取，进而进行基于 3D 内容的检索、组合重用等。在激光遥感领域，同样需要对地面、物体首先进行分类处理，然后才能进行后期地物的识别、重建

• 总之，分割采用分而治之的思想，在点云处理中和滤波一样属于重要的基础操作，在 PCL 中目前实现了进行分割的基础架构，为后期更多的扩展奠定了基础，现有实现的分割算法是鲁棒性比较好的 Cluster 聚类分割和 RANSAC 基于随机采样一致性的分割

• PCL 分割库包含多种算法，这些算法用于将点云分割为不同簇。适合处理由多个隔离区域空间组成的点云。将点云分解成其组成部分，然后可以对其进行独立处理。下面这两个图说明了平面模型分割（上）和圆柱模型分割（下）的结果

2. 平面模型分割

• planar_segmentation.cpp

  #include 
  #include 
  #include 
  #include 
  #include 
  #include 
  #include int main(int argc, char *argv[]) {pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);// 生成 15 个无序点云，x，y 为随机数，z 为 1.0cloud->width = 15;cloud->height = 1;cloud->points.resize(cloud->width * cloud->height);for (size_t i = 0; i < cloud->points.size(); ++i) {cloud->points[i].x = 1024 * rand() / (RAND_MAX + 1.0f);cloud->points[i].y = 1024 * rand() / (RAND_MAX + 1.0f);cloud->points[i].z = 1.0;}// 将 points 中 0、3、6 索引位置的 z 值进行修改，将之作为离群值cloud->points[0].z = 2.0;cloud->points[3].z = -2.0;cloud->points[6].z = 4.0;std::cerr << "Point cloud data: " << cloud->points.size() << " points" << std::endl;for (std::size_t i = 0; i < cloud->points.size(); ++i) {std::cerr << "  " << cloud->points[i].x<< " " << cloud->points[i].y<< " " << cloud->points[i].z << std::endl;}// 将输入的点云数据拟合成一个平面模型并返回该平面模型的系数// coefficients 用于存储平面模型的系数；inliers 用于存储被拟合平面包含的点的索引pcl::ModelCoefficients::Ptr coefficients(new pcl::ModelCoefficients);pcl::PointIndices::Ptr inliers(new pcl::PointIndices);pcl::SACSegmentation<pcl::PointXYZ> seg;seg.setOptimizeCoefficients(true);    // 启用平面模型系数优化seg.setModelType(pcl::SACMODEL_PLANE);    // 设置模型类型为平面模型seg.setMethodType(pcl::SAC_RANSAC);    // 采用 RANSAC 算法进行估计，因为 RANSAC 比较简单// 设置点到平面距离的阈值，用于确定属于平面的点集// 只要点到 z=1 平面距离小于该阈值的点都作为内点看待，而大于该阁值的则看做离群点seg.setDistanceThreshold(0.01);    seg.setInputCloud(cloud);// 开始分割// 将符合条件的点集索引存储在 inliers 中，平面模型系数存储在 coefficients 中seg.segment(*inliers, *coefficients);if (inliers->indices.size() == 0) {PCL_ERROR("Could not estimate a planar model for the given dataset.");return (-1);}// 此段代码用来打印出估算的平面模型的参数（以 ax+by+ca+d=0 形式）// 详见 RANSAC 采样一致性算法的 SACMODEL_PLANE 平面模型std::cerr << "Model coefficients: " << coefficients->values[0] << " "<< coefficients->values[1] << " "<< coefficients->values[2] << " "<< coefficients->values[3] << std::endl;std::cerr << "Model inliers: " << inliers->indices.size() << std::endl;for (std::size_t i = 0; i < inliers->indices.size(); ++i) {std::cerr << inliers->indices[i] << "   " << cloud->points[inliers->indices[i]].x<< " " << cloud->points[inliers->indices[i]].y<< " " << cloud->points[inliers->indices[i]].z << std::endl;}return 0;
  }
  

• 配置文件 CMakeLists.txt

  cmake_minimum_required(VERSION 3.5 FATAL_ERROR)project(planar_segmentation)find_package(PCL 1.2 REQUIRED)include_directories(${PCL_INCLUDE_DIRS})
  link_directories(${PCL_LIBRARY_DIRS})
  add_definitions(${PCL_DEFINITIONS})add_executable (planar_segmentation planar_segmentation.cpp)
  target_link_libraries (planar_segmentation ${PCL_LIBRARIES})
  

• 编译并执行

  $ mkdir build
  $ cd build
  $ cmake ..
  $ make$ ./planar_segmentation
  

  # 输出结果
  Point cloud data: 15 points0.352222 -0.151883 2-0.106395 -0.397406 1-0.473106 0.292602 1-0.731898 0.667105 -20.441304 -0.734766 10.854581 -0.0361733 1-0.4607 -0.277468 4-0.916762 0.183749 10.968809 0.512055 1-0.998983 -0.463871 10.691785 0.716053 10.525135 -0.523004 10.439387 0.56706 10.905417 -0.579787 10.898706 -0.504929 1
  Model coefficients: 0 0 1 -1
  Model inliers: 12
  1    -0.106395 -0.397406 1
  2    -0.473106 0.292602 1
  4    0.441304 -0.734766 1
  5    0.854581 -0.0361733 1
  7    -0.916762 0.183749 1
  8    0.968809 0.512055 1
  9    -0.998983 -0.463871 1
  10    0.691785 0.716053 1
  11    0.525135 -0.523004 1
  12    0.439387 0.56706 1
  13    0.905417 -0.579787 1
  14    0.898706 -0.504929 1
  

3. 圆柱体模型分割

本例介绍了如何采用 RANSAC 估计从带有噪声的点云中提取一个圆柱体模型，整个程序处理流程如下

• 过滤掉远于 1.5m 的数据点
• 估计每个点的表面法线
• 分割出平面模型（数据集中的桌面）并保存到磁盘中
• 分割圆出柱体模型（数据集中的杯子）并保存到磁盘中

• cylinder_segmentation.cpp

#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include typedef pcl::PointXYZ PointT;int main(int argc, char *argv[]) {pcl::PCDReader reader;    // PCD 文件读取对象pcl::PassThrough<PointT> pass;    // 直通滤波器pcl::NormalEstimation<PointT, pcl::Normal> ne;    // 法线估算对象pcl::SACSegmentationFromNormals<PointT, pcl::Normal> seg;    // 分割器pcl::PCDWriter writer;                                       // PCD 文件写入对象pcl::ExtractIndices<PointT> extract;                         // 点提取对象pcl::ExtractIndices<pcl::Normal> extract_normals;            // 法线提取对象pcl::search::KdTree<PointT>::Ptr tree(new pcl::search::KdTree<PointT>());pcl::PointCloud<PointT>::Ptr cloud(new pcl::PointCloud<PointT>);pcl::PointCloud<PointT>::Ptr cloud_filtered(new pcl::PointCloud<PointT>);pcl::PointCloud<pcl::Normal>::Ptr cloud_normals(new pcl::PointCloud<pcl::Normal>);pcl::PointCloud<PointT>::Ptr cloud_filtered2(new pcl::PointCloud<PointT>);pcl::PointCloud<pcl::Normal>::Ptr cloud_normals2(new pcl::PointCloud<pcl::Normal>);pcl::ModelCoefficients::Ptr coefficients_plane(new pcl::ModelCoefficients), coefficients_cylinder(new pcl::ModelCoefficients);pcl::PointIndices::Ptr inliers_plane(new pcl::PointIndices), inliers_cylinder(new pcl::PointIndices);// 读取点云数据reader.read("../data/table_scene_mug_stereo_textured.pcd", *cloud);std::cerr << "PointCloud has: " << cloud->points.size() << " data points." << std::endl;// 构建直通滤波器去除伪 NaNspass.setInputCloud(cloud);pass.setFilterFieldName("z");pass.setFilterLimits(0, 1.5);pass.filter(*cloud_filtered);std::cerr << "PointCloud after filtering has: " << cloud_filtered->points.size() << " data points." << std::endl;// 估计点的法线ne.setSearchMethod(tree);ne.setInputCloud(cloud_filtered);ne.setKSearch(50);ne.compute(*cloud_normals);// 为平面模型创建分割对象，并设置所有参数seg.setOptimizeCoefficients(true);    // 启用平面模型系数（平面方程的系数）的优化seg.setModelType(pcl::SACMODEL_NORMAL_PLANE);    // 设置模型类型为法向量估计的平面模型// 设置法向量距离权重系数为 0.1，表示在拟合平面的时候更加注重法向量的一致性// 即：使得拟合出来的平面法向量与原始点云法向量的夹角最小seg.setNormalDistanceWeight(0.1);seg.setModelType(pcl::SAC_RANSAC);seg.setMaxIterations(100);seg.setDistanceThreshold(0.03);    // 设置距离阈值为 0.03，表示与平面拟合误差超过该值的点将被视为离群点seg.setInputCloud(cloud_filtered);seg.setInputNormals(cloud_normals);// 将拟合出来的平面模型系数存储到 coefficients_plane 中，同时将属于平面的点的索引存储到 inliers_plane 中seg.segment(*inliers_plane, *coefficients_plane);std::cerr << "Plane coefficients: " << *coefficients_plane << std::endl;    // 输出拟合出来的平面模型系数到控制台// 从输入点云中提取平面内点extract.setInputCloud(cloud_filtered);extract.setIndices(inliers_plane);extract.setNegative(false);// 将提取到的平面内点写入磁盘pcl::PointCloud<PointT>::Ptr cloud_plane(new pcl::PointCloud<PointT>());extract.filter(*cloud_plane);std::cerr << "PointCloud representing the planar component: " << cloud_plane->points.size() << " data points."<< std::endl;writer.write("table_scene_mug_stereo_textured_plane.pcd", *cloud_plane, false);// 去除平面内点，提取剩余的点extract.setNegative(true);extract.filter(*cloud_filtered2);extract_normals.setNegative(true);extract_normals.setInputCloud(cloud_normals);extract_normals.setIndices(inliers_plane);extract_normals.filter(*cloud_normals2);// 创建用于圆柱体分割的分割对象，并设置所有参数seg.setOptimizeCoefficients(true);seg.setModelType(pcl::SACMODEL_CYLINDER);   // 设置分割模型为圆柱体seg.setMethodType(pcl::SAC_RANSAC);         // 设置采用 RANSAC 算法进行参数估计seg.setNormalDistanceWeight(0.1);           // 设置表面法线权重系数seg.setMaxIterations(10000);                // 设置最大迭代次数 10000seg.setDistanceThreshold(0.05);             // 设置内点到模型的最大距离 0.05mseg.setRadiusLimits(0, 0.1);                // 设置圆柱体的半径范围 0 ~ 0.1mseg.setInputCloud(cloud_filtered2);seg.setInputNormals(cloud_normals2);seg.segment(*inliers_cylinder, *coefficients_cylinder);std::cerr << "Cylinder coefficients: " << *coefficients_cylinder << std::endl;// 将圆柱体内点写入磁盘extract.setInputCloud(cloud_filtered2);extract.setIndices(inliers_cylinder);extract.setNegative(false);pcl::PointCloud<PointT>::Ptr cloud_cylinder(new pcl::PointCloud<PointT>());extract.filter(*cloud_cylinder);if (cloud_cylinder->points.empty()) {std::cerr << "Can't find the cylindrical component." << std::endl;} else {std::cerr << "PointCloud representing the cylindrical component: " << cloud_cylinder->points.size()<< " data points." << std::endl;writer.write("table_scene_mug_stereo_textured_cylinder.pcd", *cloud_cylinder, false);}return 0;
}

• 配置文件 CMakeLists.txt

  cmake_minimum_required(VERSION 3.5 FATAL_ERROR)project(cylinder_segmentation)find_package(PCL 1.2 REQUIRED)include_directories(${PCL_INCLUDE_DIRS})
  link_directories(${PCL_LIBRARY_DIRS})
  add_definitions(${PCL_DEFINITIONS})add_executable (cylinder_segmentation cylinder_segmentation.cpp)
  target_link_libraries (cylinder_segmentation ${PCL_LIBRARIES})
  

• 编译并执行

  $ mkdir build
  $ cd build
  $ cmake ..
  $ make$ ./cylinder_segmentation
  

  # 输出结果
  PointCloud has: 307200 data points.
  PointCloud after filtering has: 139897 data points.
  Plane coefficients: header: 
  seq: 0 stamp: 0 frame_id: 
  values[]values[0]:   0.015758values[1]:   -0.838789values[2]:   -0.544229values[3]:   0.527018PointCloud representing the planar component: 126168 data points.
  Cylinder coefficients: header: 
  seq: 0 stamp: 0 frame_id: 
  values[]values[0]:   0.0585808values[1]:   0.279481values[2]:   0.900414values[3]:   -0.0129607values[4]:   -0.843949values[5]:   -0.536267values[6]:   0.0387611PointCloud representing the cylindrical component: 9271 data points.
  

  # 将三个点云在一个窗口内显示
  $ pcl_viewer ../data/table_scene_mug_stereo_textured.pcd table_scene_mug_stereo_textured_plane.pcd table_scene_mug_stereo_textured_cylinder.pcd
  

4. 欧式聚类提取

• cluster_extraction.cpp

#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include int main(int argc, char **argv) {// 读取输入点云pcl::PCDReader reader;pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>), cloud_f(new pcl::PointCloud<pcl::PointXYZ>);reader.read("./data/tabletop.pcd", *cloud);std::cout << "PointCloud before filtering has: " << cloud->points.size() << " data points." << std::endl; //*// 执行降采样滤波，叶子大小 1cmpcl::VoxelGrid<pcl::PointXYZ> vg;pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered(new pcl::PointCloud<pcl::PointXYZ>);vg.setInputCloud(cloud);vg.setLeafSize(0.01f, 0.01f, 0.01f);vg.filter(*cloud_filtered);std::cout << "PointCloud after filtering has: " << cloud_filtered->points.size() << " data points." << std::endl;// 创建平面模型分割器并初始化参数pcl::SACSegmentation<pcl::PointXYZ> seg;pcl::PointIndices::Ptr inliers(new pcl::PointIndices);pcl::ModelCoefficients::Ptr coefficients(new pcl::ModelCoefficients);pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_plane(new pcl::PointCloud<pcl::PointXYZ>());pcl::PCDWriter writer;seg.setOptimizeCoefficients(true);seg.setModelType(pcl::SACMODEL_PLANE);seg.setMethodType(pcl::SAC_RANSAC);seg.setMaxIterations(100);seg.setDistanceThreshold(0.02);int i = 0, nr_points = (int) cloud_filtered->points.size();while (cloud_filtered->points.size() > 0.3 * nr_points) {// 分割出剩余点云中最大的平面seg.setInputCloud(cloud_filtered);// 执行分割，将分割出来的平面点云索引保存到 inliers 中seg.segment(*inliers, *coefficients);if (inliers->indices.size() == 0) {std::cout << "Could not estimate a planar model for the given dataset." << std::endl;break;}// 从输入点云中取出平面内点pcl::ExtractIndices<pcl::PointXYZ> extract;extract.setInputCloud(cloud_filtered);extract.setIndices(inliers);extract.setNegative(false);// 得到与 cloud_plane 平面相关的点extract.filter(*cloud_plane);std::cout << "PointCloud representing the planar component: " << cloud_plane->points.size() << " data points." << std::endl;// 从点云中剔除这些平面内点，提取出剩下的点保存到 cloud_f 中，并重新赋值给 cloud_filteredextract.setNegative(true);extract.filter(*cloud_f);*cloud_filtered = *cloud_f;}// 为提取算法的搜索方法创建一个 KdTree 对象pcl::search::KdTree<pcl::PointXYZ>::Ptr tree(new pcl::search::KdTree<pcl::PointXYZ>);tree->setInputCloud(cloud_filtered);/** cluster_indices 是一个 vector，包含多个检测到的簇的 PointIndices 的实例* 因此，cluster_indices[0] 包含点云中第一个 cluster(簇)的所有索引* 从点云中提取簇（集群），并将点云索引保存在 cluster_indices 中*/std::vector<pcl::PointIndices> cluster_indices;pcl::EuclideanClusterExtraction<pcl::PointXYZ> ec;// 如果搜索半径取一个非常小的值，那么一个实际独立的对象就会被分割为多个聚类；如果将值设置得太高，那么多个对象就会被分割为一个聚类ec.setClusterTolerance(0.02);        // 设置聚类搜索半径（搜索容差）为 2cmec.setMinClusterSize(100);           // 每个簇（集群）的最小 大小，限制一个聚类最少需要的点数目ec.setMaxClusterSize(25000);         // 每个簇（集群）的最大 大小，限制一个聚类最多需要的点数目ec.setSearchMethod(tree);            // 设置点云搜索算法ec.setInputCloud(cloud_filtered);    // 设置输入点云ec.extract(cluster_indices);         // 设置提取到的簇，将每个簇以索引的形式保存到 cluster_indicespcl::visualization::PCLVisualizer::Ptr viewer(new pcl::visualization::PCLVisualizer("3D Viewer"));// 将一个点云数据集中的点根据聚类结果，划分为多个簇，并可视化显示每个簇int j = 0;for (std::vector<pcl::PointIndices>::const_iterator it = cluster_indices.begin();it != cluster_indices.end(); ++it) {// 每次创建一个新的点云数据集，并将所有当前簇的点写入到点云数据集中pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_cluster(new pcl::PointCloud<pcl::PointXYZ>);const std::vector<int> &indices = it->indices;for (std::vector<int>::const_iterator pit = indices.begin(); pit != indices.end(); ++pit) {cloud_cluster->points.push_back(cloud_filtered->points[*pit]);}// 输出当前簇所包含的点的数量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 << "cloud_cluster_" << j;// 生成随机颜色pcl::visualization::PointCloudColorHandlerRandom<pcl::PointXYZ> single_color(cloud_cluster);viewer->addPointCloud<pcl::PointXYZ>(cloud_cluster, single_color, ss.str());viewer->setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 2, ss.str());j++;  // 增加计数器 j，以便为下一个簇创建一个不同的名称}std::cout << "cloud size: " << cluster_indices.size() << std::endl;viewer->addCoordinateSystem(0.5);while (!viewer->wasStopped()) {viewer->spinOnce();}return (0);
}

• 配置文件 CMakeLists.txt

  cmake_minimum_required(VERSION 3.5 FATAL_ERROR)project(cluster_extraction)find_package(PCL 1.2 REQUIRED)include_directories(${PCL_INCLUDE_DIRS})
  link_directories(${PCL_LIBRARY_DIRS})
  add_definitions(${PCL_DEFINITIONS})add_executable (cluster_extraction cluster_extraction.cpp)
  target_link_libraries (cluster_extraction ${PCL_LIBRARIES})
  

• 编译并执行

  $ mkdir build
  $ cd build
  $ cmake ..
  $ make$ ./cluster_extraction
  

  # 输出结果
  PointCloud before filtering has: 460400 data points.
  PointCloud after filtering has: 41049 data points.
  PointCloud representing the planar component: 20536 data points.
  PointCloud representing the planar component: 12442 data points.
  PointCloud representing the Cluster: 4857 data points.
  PointCloud representing the Cluster: 1386 data points.
  PointCloud representing the Cluster: 321 data points.
  PointCloud representing the Cluster: 291 data points.
  PointCloud representing the Cluster: 123 data points.
  cloud size: 5
  

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