PCL欧几里德点云簇提取

最新推荐文章于 2025-01-17 17:32:27 发布

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最新推荐文章于 2025-01-17 17:32:27 发布 · 446 阅读

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本文详细介绍了如何利用Point Cloud Library (PCL) 进行欧几里德聚类，该过程涉及到点云数据的预处理、距离阈值设定以及簇的检测，帮助用户有效地从点云数据中提取出独立的对象。


#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>
#include <pcl/visualization/pcl_visualizer.h>


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("C:\\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;  // 体元
    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered(new pcl::PointCloud<pcl::PointXYZ>);
    vg.setInputCloud(cloud)