PCL点云分割（1） / 开普饭

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，例如逆向工作，CAD领域对零件的不同扫描表面进行分割，然后才能更好的进行空洞修复曲面重建，特征描述和提取，进而进行基于3D内容的检索，组合重用等。

案例分析

用一组点云数据做简单的平面的分割：

#include <iostream>
#include <pcl/ModelCoefficients.h>
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <pcl/sample_consensus/method_types.h> //随机参数估计方法头文件
#include <pcl/sample_consensus/model_types.h> //模型定义头文件
#include <pcl/segmentation/sac_segmentation.h> //基于采样一致性分割的类的头文件

int main (int argc, char** argv) { pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>); // 填充点云 cloud->width = 15; cloud->height = 1; cloud->points.resize (cloud->width * cloud->height);
// 生成数据，采用随机数填充点云的x,y坐标，都处于z为1的平面上 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; }
// 设置几个局外点，即重新设置几个点的z值，使其偏离z为1的平面 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; //打印 for (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;

//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<pcl::PointXYZ> seg; // 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
//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); if (inliers->indices.size () == 0) { PCL_ERROR ("Could not estimate a planar model for the given dataset."); return (-1); } //打印出平面模型 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 (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); }

结果如下：开始打印的数据为手动添加的点云数据，并非都处于z为1的平面上，通过分割对象的处理后提取所有内点，即过滤掉z不等于1的点集

（2）实现圆柱体模型的分割：采用随机采样一致性估计从带有噪声的点云中提取一个圆柱体模型。

#include <pcl/ModelCoefficients.h>
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <pcl/filters/extract_indices.h>
#include <pcl/filters/passthrough.h>
#include <pcl/features/normal_3d.h>
#include <pcl/sample_consensus/method_types.h>
#include <pcl/sample_consensus/model_types.h>
#include <pcl/segmentation/sac_segmentation.h>

typedef pcl::PointXYZ PointT;intmain (int argc, char** argv) { // All the objects needed pcl::PCDReader reader; //PCD文件读取对象 pcl::PassThrough<PointT> pass; //直通滤波对象 pcl::NormalEstimation<PointT, pcl::Normal> ne; //法线估计对象 pcl::SACSegmentationFromNormals<PointT, pcl::Normal> seg; //分割对象 pcl::PCDWriter writer; //PCD文件读取对象 pcl::ExtractIndices<PointT> extract; //点提取对象 pcl::ExtractIndices<pcl::Normal> extract_normals; ///点提取对象 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); // Read in the cloud data reader.read ("table_scene_mug_stereo_textured.pcd", *cloud); std::cerr << "PointCloud has: " << cloud->points.size () << " data points." << std::endl;

// 直通滤波，将Z轴不在（0，1.5）范围的点过滤掉，将剩余的点存储到cloud_filtered对象中 pass.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;

// 过滤后的点云进行法线估计，为后续进行基于法线的分割准备数据
ne.setSearchMethod (tree); ne.setInputCloud (cloud_filtered); ne.setKSearch (50); ne.compute (*cloud_normals);

// Create the segmentation object for the planar model and set all the parameters seg.setOptimizeCoefficients (true); seg.setModelType (pcl::SACMODEL_NORMAL_PLANE); seg.setNormalDistanceWeight (0.1); seg.setMethodType (pcl::SAC_RANSAC); seg.setMaxIterations (100); seg.setDistanceThreshold (0.03); seg.setInputCloud (cloud_filtered); seg.setInputNormals (cloud_normals); //获取平面模型的系数和处在平面的内点 seg.segment (*inliers_plane, *coefficients_plane); std::cerr << "Plane coefficients: " << *coefficients_plane << std::endl;

// 从点云中抽取分割的处在平面上的点集
extract.setInputCloud (cloud_filtered); extract.setIndices (inliers_plane); extract.setNegative (false); // 存储分割得到的平面上的点到点云文件 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);

// Remove the planar inliers, extract the rest 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);

// Create the segmentation object for cylinder segmentation and set all the parameters seg.setOptimizeCoefficients (true); //设置对估计模型优化 seg.setModelType (pcl::SACMODEL_CYLINDER); //设置分割模型为圆柱形 seg.setMethodType (pcl::SAC_RANSAC); //参数估计方法 seg.setNormalDistanceWeight (0.1); //设置表面法线权重系数 seg.setMaxIterations (10000); //设置迭代的最大次数10000 seg.setDistanceThreshold (0.05); //设置内点到模型的距离允许最大值 seg.setRadiusLimits (0, 0.1); //设置估计出的圆柱模型的半径的范围 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;

// Write the cylinder inliers to disk
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); }

打印的结果如下

原始点云可视化的结果.三维场景中有平面，杯子，和其他物体

产生分割以后的平面和圆柱点云，查看的结果如下

（3）PCL中实现欧式聚类提取。对三维点云组成的场景进行分割

#include <pcl/ModelCoefficients.h>
#include <pcl/point_types.h>
#include <pcl/io/pcd_io.h>
#include <pcl/filters/extract_indices.h>
#include <pcl/filters/voxel_grid.h>
#include <pcl/features/normal_3d.h>
#include <pcl/kdtree/kdtree.h>
#include <pcl/sample_consensus/method_types.h>
#include <pcl/sample_consensus/model_types.h>
#include <pcl/segmentation/sac_segmentation.h>
#include<pcl/segmentation/extract_clusters.h>
/*打开点云数据，并对点云进行滤波重采样预处理，然后采用平面分割模型对点云进行分割处理提取出点云中所有在平面上的点集，并将其存盘**/

int main (int argc, char** argv) {
// Read in the cloud data
pcl::PCDReader reader; pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>), cloud_f (new pcl::PointCloud<pcl::PointXYZ>);
reader.read ("table_scene_lms400.pcd", *cloud); std::cout << "PointCloud before filtering has: " << cloud->points.size () << " data points." << std::endl; //*

//Create the filtering object: downsample the dataset using a leaf size of 1cm pcl::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; //*

//创建平面模型分割的对象并设置参数 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 (newpcl::PointCloud<pcl::PointXYZ> ()); pcl::PCDWriter writer; seg.setOptimizeCoefficients (true); seg.setModelType (pcl::SACMODEL_PLANE); //分割模型 seg.setMethodType (pcl::SAC_RANSAC); //随机参数估计方法 seg.setMaxIterations (100); //最大的迭代的次数 seg.setDistanceThreshold (0.02); //设置阀值 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 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; } pcl::ExtractIndices<pcl::PointXYZ> extract; extract.setInputCloud (cloud_filtered); extract.setIndices (inliers); extract.setNegative (false);

// Get the points associated with the planar surface extract.filter (*cloud_plane); std::cout << "PointCloud representing the planar component: " << cloud_plane->points.size () << " data points." << std::endl; //

// 移去平面局内点，提取剩余点云 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<pcl::PointXYZ>::Ptr tree (new pcl::search::KdTree<pcl::PointXYZ>); tree->setInputCloud (cloud_filtered); std::vector<pcl::PointIndices> cluster_indices; pcl::EuclideanClusterExtraction<pcl::PointXYZ> ec; //欧式聚类对象 ec.setClusterTolerance (0.02); // 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的点数目为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

//迭代访问点云索引cluster_indices,直到分割处所有聚类 int j = 0;
for (std::vector<pcl::PointIndices>::const_iterator it = cluster_indices.begin (); it != cluster_indices.end (); ++it) { pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_cluster (new pcl::PointCloud<pcl::PointXYZ>);

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 << "cloud_cluster_" << j << ".pcd"; writer.write<pcl::PointXYZ> (ss.str (), *cloud_cluster, false); //* j++; }
return (0); }

运行结果：

不再一一查看可视化的结果

不小心把这一篇放在后面发了，这也是基础知识，似乎公众号可以评论了，因为申请了原创保护，当然我还是那一句话，希望大家能够分享关于点云的知识，比如论文，需要解决的应用等等，分享才是硬道理！

PCL点云分割（1）

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