PCL 基于欧几里得聚类的点云分割

这个程序可以提取出PCL点云里的欧式聚类，从而分割点云。

先看一下输入点云，是一张桌子：

程序运行后会提取出欧几里得聚类，如下图所示：

桌子本来是立体的，现在只有个截面了：

下面是同时显示输入点云和输出点云的结果，作为对比：

粉色的是输入点云，其他颜色的是输出点云。

输入：table_scene_lms400.pcd PCL点云

输出：cloud_cluster_000X.pcd X个 PCL点云

代码：

#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/search/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> #include <iomanip> // for setw, setfill int main () { // 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->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->size () << " data points." << std::endl; //* // Create the segmentation object for the planar model and set all the parameters 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 nr_points = (int) cloud_filtered->size (); while (cloud_filtered->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; } // Extract the planar inliers from the input cloud 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->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<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; ec.setClusterTolerance (0.02); // 2cm ec.setMinClusterSize (100); ec.setMaxClusterSize (25000); ec.setSearchMethod (tree); ec.setInputCloud (cloud_filtered); ec.extract (cluster_indices); int j = 0; for (const auto& cluster : cluster_indices) { pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_cluster (new pcl::PointCloud<pcl::PointXYZ>); for (const auto& idx : cluster.indices) { cloud_cluster->push_back((*cloud_filtered)[idx]); } //* cloud_cluster->width = cloud_cluster->size (); cloud_cluster->height = 1; cloud_cluster->is_dense = true; std::cout << "PointCloud representing the Cluster: " << cloud_cluster->size () << " data points." << std::endl; std::stringstream ss; ss << std::setw(4) << std::setfill('0') << j; writer.write<pcl::PointXYZ> ("cloud_cluster_" + ss.str () + ".pcd", *cloud_cluster, false); //* j++; } return (0); }

参考：Euclidean Cluster Extraction — Point Cloud Library 0.0 documentation