用体元滤波器进行降低采样

我们这次用voxel filter(体元滤波器)来滤波

#include <iostream>
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <pcl/filters/voxel_grid.h>

int
main (int argc, char** argv)
{
  pcl::PCLPointCloud2::Ptr cloud (new pcl::PCLPointCloud2 ());
  pcl::PCLPointCloud2::Ptr cloud_filtered (new pcl::PCLPointCloud2 ());

  // Fill in the cloud data
  pcl::PCDReader reader;
  // Replace the path below with the path where you saved your file
  reader.read ("table_scene_lms400.pcd", *cloud); // Remember to download the file first!

  std::cerr << "PointCloud before filtering: " << cloud->width * cloud->height 
       << " data points (" << pcl::getFieldsList (*cloud) << ").";

  // Create the filtering object
  pcl::VoxelGrid<pcl::PCLPointCloud2> sor;
  sor.setInputCloud (cloud);
  sor.setLeafSize (0.01f, 0.01f, 0.01f);
  sor.filter (*cloud_filtered);

  std::cerr << "PointCloud after filtering: " << cloud_filtered->width * cloud_filtered->height 
       << " data points (" << pcl::getFieldsList (*cloud_filtered) << ").";

  pcl::PCDWriter writer;
  writer.write ("table_scene_lms400_downsampled.pcd", *cloud_filtered, 
         Eigen::Vector4f::Zero (), Eigen::Quaternionf::Identity (), false);

  return (0);
}

以下是一些解释

从磁盘中读取文件

  // Fill in the cloud data
  pcl::PCDReader reader;
  // Replace the path below with the path where you saved your file
  reader.read ("table_scene_lms400.pcd", *cloud); // Remember to download the file first!

然后我们用了VoxelGrid这个滤波器，过滤的尺寸为1cm

  pcl::VoxelGrid<pcl::PCLPointCloud2> sor;
  sor.setInputCloud (cloud);
  sor.setLeafSize (0.01f, 0.01f, 0.01f);
  sor.filter (*cloud_filtered);

最终把数据存到磁盘里面

  pcl::PCDWriter writer;
  writer.write ("table_scene_lms400_downsampled.pcd", *cloud_filtered, 
         Eigen::Vector4f::Zero (), Eigen::Quaternionf::Identity (), false);

运行结果，可以看到这把计算量降低到原来的大约十分之一

PointCloud before filtering: 460400 data points (x y z intensity distance sid).
PointCloud after filtering: 41049 data points (x y z intensity distance sid).