1.版本要求

版本: >PCL1.3

2.简介

欧式聚类是点云聚类的一种重要方法，利用点云中点与点之间的欧式距离进行聚类，当点与点之间的欧式距离小于设定的阈值则视为一类。欧式聚类是车辆前方障碍物检测的重要方法。

3.数据

本例中使用的点云数据（test.pcd）请见百度网盘分享。
链接：https://pan.baidu.com/s/1io3q_ESUbhdGT2vr6-NuVA
提取码：ias2

4.代码

#include <pcl/point_types.h>
#include <pcl/io/pcd_io.h>
#include <pcl/kdtree/kdtree.h>
#include <pcl/segmentation/extract_clusters.h>
#include <pcl/visualization/cloud_viewer.h>int main(int argc, char** argv)
{// 读取测试点云pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);pcl::PCDReader reader;reader.read("test.pcd", *cloud);// 创建KdTreea对象用于点云搜索pcl::search::KdTree<pcl::PointXYZ>::Ptr tree(new pcl::search::KdTree<pcl::PointXYZ>);tree->setInputCloud(cloud);std::vector<pcl::PointIndices> cluster_indices;  //创建索引对象向量，用于存储不同聚类结果的点云索引pcl::EuclideanClusterExtraction<pcl::PointXYZ> ec;  //创建欧式聚类对象ec.setClusterTolerance(0.13); // 设置距离阈值为13cm。点与点之间小于这个距离阈值视为一类ec.setMinClusterSize(200);  //设置聚类最少点数ec.setMaxClusterSize(5000);  //设置聚类最大点数ec.setSearchMethod(tree);  //输入点云搜索方法ec.setInputCloud(cloud);ec.extract(cluster_indices);  //开始聚类//将聚类结果合成一幅点云，方便后面对比显示pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_cluster(new pcl::PointCloud<pcl::PointXYZ>);for (std::vector<pcl::PointIndices>::const_iterator it = cluster_indices.begin(); it != cluster_indices.end(); ++it)  //循环一次就是一个聚类结果{for (std::vector<int>::const_iterator pit = it->indices.begin(); pit != it->indices.end(); ++pit)  cloud_cluster->push_back((*cloud)[*pit]);  //每个聚类结果都装到cloud_cluster这幅点云里}cloud_cluster->width = cloud_cluster->size();  //点云尺寸的明确，存储点云前需要明确尺寸大小cloud_cluster->height = 1;cloud_cluster->is_dense = true;//对比显示聚类结果pcl::visualization::PCLVisualizer viewer("Cloud Viewer");int v1(0);  //创建左窗口显示原始点云viewer.createViewPort(0, 0, 0.5, 1.0, v1);  //左右窗口大小划分，1:1viewer.setBackgroundColor(0, 0, 0, v1);viewer.addText("Original Cloud", 2, 2, "Original Cloud", v1);pcl::visualization::PointCloudColorHandlerGenericField<pcl::PointXYZ> rgb1(cloud, "z");viewer.addPointCloud<pcl::PointXYZ>(cloud, rgb1, "original cloud", v1);viewer.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 1, "original cloud", v1);viewer.addCoordinateSystem(1.0, "original cloud", v1);int v2(1);  //创建右窗口显示聚类结果viewer.createViewPort(0.5, 0, 1.0, 1.0, v2);  //左右窗口大小划分，1:1viewer.setBackgroundColor(0, 0, 0, v2);viewer.addText("Clustered Cloud", 2, 2, "Clustered Cloud", v2);pcl::visualization::PointCloudColorHandlerGenericField<pcl::PointXYZ> rgb2(cloud_cluster, "z");viewer.addPointCloud<pcl::PointXYZ>(cloud_cluster, rgb2, "clustered cloud", v2);viewer.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 1, "clustered cloud", v2);viewer.addCoordinateSystem(1.0, "clustered cloud", v2);viewer.spin(); //循环不断显示点云return (0);
}