PCL 平面分割

#include <iostream>
#include <pcl/ModelCoefficients.h>
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <pcl/sample_consensus/method_types.h>
#include <pcl/sample_consensus/model_types.h>
#include <pcl/segmentation/sac_segmentation.h>
#include <pcl/filters/voxel_grid.h>
#include <pcl/filters/extract_indices.h>
int
main (int argc, char** argv)
{
   pcl::PointCloud<pcl::PointXYZI>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZI>);
   pcl::PointCloud<pcl::PointXYZI>::Ptr cloud_filtered(new pcl::PointCloud<pcl::PointXYZI>),cloud_p (new pcl::PointCloud<pcl::PointXYZI>), cloud_f (new pcl::PointCloud<pcl::PointXYZI>);
   // 填入点云数据
   pcl::io::loadPCDFile("table_scene_lms400.pcd", *cloud);

   // 创建滤波器对象
   pcl::VoxelGrid<pcl::PointXYZI> sor;//滤波处理对象
   sor.setInputCloud(cloud);
   sor.setLeafSize(0.01f, 0.01f, 0.01f);//设置滤波器处理时采用的体素大小的参数
   sor.filter(*cloud_filtered);
  std::cerr << "PointCloud after filtering: " << cloud_filtered->width * cloud_filtered->height << " data points." << std::endl;
  // 将下采样后的数据存入磁盘
  pcl::PCDWriter writer;
  writer.write<pcl::PointXYZI> ("table_scene_lms400_downsampled.pcd", *cloud_filtered, false);
  pcl::ModelCoefficients::Ptr coefficients (new pcl::ModelCoefficients ());
  pcl::PointIndices::Ptr inliers (new pcl::PointIndices ());
  // 创建分割对象
  pcl::SACSegmentation<pcl::PointXYZI> seg;
  // 可选
  seg.setOptimizeCoefficients (true);
  // 必选
  seg.setModelType (pcl::SACMODEL_PLANE);
  seg.setMethodType (pcl::SAC_RANSAC);
  seg.setMaxIterations (1000);
  seg.setDistanceThreshold (0.01);
  // 创建滤波器对象
  pcl::ExtractIndices<pcl::PointXYZI> extract;
  int i = 0, nr_points = (int) cloud_filtered->points.size ();
  // 当还有30%原始点云数据时
  while (cloud_filtered->points.size () > 0.3 * nr_points)
  {
    // 从余下的点云中分割最大平面组成部分
    seg.setInputCloud (cloud_filtered);
    seg.segment (*inliers, *coefficients);
    if (inliers->indices.size () == 0)
    {
      std::cerr << "Could not estimate a planar model for the given dataset." << std::endl;
      break;
    }
    // 分离内层
    extract.setInputCloud (cloud_filtered);
    extract.setIndices (inliers);
    extract.setNegative (false);
    extract.filter (*cloud_p);
    std::cerr << "PointCloud representing the planar component: " << cloud_p->width * cloud_p->height << " data points." << std::endl;
    std::stringstream ss;
    ss << "table_scene_lms400_plane_" << i << ".pcd";
    writer.write<pcl::PointXYZI> (ss.str (), *cloud_p, false);
    // 创建滤波器对象
    extract.setNegative (true);
    extract.filter (*cloud_f);
    cloud_filtered.swap (cloud_f);
    i++;
  }
  return (0);
}