3d激光雷达开发（基于统计滤波）

Posted 2022-03-10 费晓行

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        基于统计方法的滤波其实是比较好理解的。也就是说，在一个局部范围内，选择距离最近的几十个点，要求他们的平均值必须小于一个数值。这样，无形之中就把那些离散点给去除了。从原理上说，这个算法比较直观。但是它最大的问题就是计算时间太长。我一开始运行后，都以为程序不对了，结果是pc运行时间太长导致的。原来的代码地址在这，https://pcl.readthedocs.io/projects/tutorials/en/master/statistical_outlier.html#statistical-outlier-removal

1、准备statistical_removal.cpp文件

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

int
main ()

  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);
  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered (new pcl::PointCloud<pcl::PointXYZ>);

  // Fill in the cloud data
  pcl::PCDReader reader;
  // Replace the path below with the path where you saved your file
  reader.read<pcl::PointXYZ> ("table_scene_lms400.pcd", *cloud);

  std::cerr << "Cloud before filtering: " << std::endl;
  std::cerr << *cloud << std::endl;

  // Create the filtering object
  pcl::StatisticalOutlierRemoval<pcl::PointXYZ> sor;
  sor.setInputCloud (cloud);
  sor.setMeanK (50);
  sor.setStddevMulThresh (1.0);
  sor.filter (*cloud_filtered);

  std::cerr << "Cloud after filtering: " << std::endl;
  std::cerr << *cloud_filtered << std::endl;

  pcl::PCDWriter writer;
  writer.write<pcl::PointXYZ> ("table_scene_lms400_inliers.pcd", *cloud_filtered, false);

  sor.setNegative (true);
  sor.filter (*cloud_filtered);
  writer.write<pcl::PointXYZ> ("table_scene_lms400_outliers.pcd", *cloud_filtered, false);

  return (0);

2、准备CMakeLists.txt文件

 cmake_minimum_required(VERSION 3.5 FATAL_ERROR)
 
 project(statistical_removal)
 
 find_package(PCL 1.2 REQUIRED)
 
 include_directories($PCL_INCLUDE_DIRS)
 link_directories($PCL_LIBRARY_DIRS)
 add_definitions($PCL_DEFINITIONS)

add_executable (statistical_removal statistical_removal.cpp)
target_link_libraries (statistical_removal $PCL_LIBRARIES)

3、编译生成sln文件，准备编译

        用cmake生成sln工程，直接编译即可

4、补充完缺失的dll，直接运行statistical_removal.exe文件