3d激光雷达开发（基于半径或者条件的滤波）

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        基于半径的滤波，主要是指某个半径的范围内，必须要有一定数量的邻居。而基于条件的滤波，这一点和PassThrough有点像，但是它可以把很多条件组合在一起，比如说同时满足x轴、y轴、z轴等条件，然后开始滤波。原来的代码在这，https://pcl.readthedocs.io/projects/tutorials/en/latest/remove_outliers.html#remove-outliers。之前的代码效果不太好，不太看得出效果，在数量和约束条件稍做修改后，效果明显。

1、准备remove_outliers.cpp文件

#include <iostream>
#include <pcl/point_types.h>
#include <pcl/filters/radius_outlier_removal.h>
#include <pcl/filters/conditional_removal.h>

int
 main (int argc, char** argv)
{
  if (argc != 2)
  {
    std::cerr << "please specify command line arg '-r' or '-c'" << std::endl;
    exit(0);
  }
  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
  cloud->width  = 100;
  cloud->height = 1;
  cloud->resize (cloud->width * cloud->height);

  for (auto& point: *cloud)
  {
    point.x = 1024 * rand () / (RAND_MAX + 1.0f);
    point.y = 1024 * rand () / (RAND_MAX + 1.0f);
    point.z = 1024 * rand () / (RAND_MAX + 1.0f);
  }

  if (strcmp(argv[1], "-r") == 0){
    pcl::RadiusOutlierRemoval<pcl::PointXYZ> outrem;
    // build the filter
    outrem.setInputCloud(cloud);
    outrem.setRadiusSearch(100);
    outrem.setMinNeighborsInRadius (1);
    outrem.setKeepOrganized(true);
    // apply filter
    outrem.filter (*cloud_filtered);
  }
  else if (strcmp(argv[1], "-c") == 0){
    // build the condition
    pcl::ConditionAnd<pcl::PointXYZ>::Ptr range_cond (new
      pcl::ConditionAnd<pcl::PointXYZ> ());
    range_cond->addComparison (pcl::FieldComparison<pcl::PointXYZ>::ConstPtr (new
      pcl::FieldComparison<pcl::PointXYZ> ("z", pcl::ComparisonOps::GT, 0.0)));
    range_cond->addComparison (pcl::FieldComparison<pcl::PointXYZ>::ConstPtr (new
      pcl::FieldComparison<pcl::PointXYZ> ("z", pcl::ComparisonOps::LT, 100.0)));
    // build the filter
    pcl::ConditionalRemoval<pcl::PointXYZ> condrem;
    condrem.setCondition (range_cond);
    condrem.setInputCloud (cloud);
    condrem.setKeepOrganized(true);
    // apply filter
    condrem.filter (*cloud_filtered);
  }
  else{
    std::cerr << "please specify command line arg '-r' or '-c'" << std::endl;
    exit(0);
  }
  std::cerr << "Cloud before filtering: " << std::endl;
  for (const auto& point: *cloud)
    std::cerr << "    " << point.x << " "
                        << point.y << " "
                        << point.z << std::endl;
  // display pointcloud after filtering
  std::cerr << "Cloud after filtering: " << std::endl;
  for (const auto& point: *cloud_filtered)
    std::cerr << "    " << point.x << " "
                        << point.y << " "
                        << point.z << std::endl;
  return (0);
}

2、准备CMakeLists.txt文件

cmake_minimum_required(VERSION 3.5 FATAL_ERROR)

project(remove_outliers)

find_package(PCL 1.2 REQUIRED)

include_directories(${PCL_INCLUDE_DIRS})
link_directories(${PCL_LIBRARY_DIRS})
add_definitions(${PCL_DEFINITIONS})

add_executable (remove_outliers remove_outliers.cpp)
target_link_libraries (remove_outliers ${PCL_LIBRARIES})

3、生成sln文件，开始编译

4、执行remove_outliers.exe文件

        -r效果显示，

        -c效果显示，

         实际效果其实是一个效果对比。一个是滤波之前，一个是滤波之后，两者还是有差别的。因为随机生成的数据比较多，一个页面没有办法完全表达完整，这里只是截取了一小段。大家可以自己编译生成可执行文件，实际运行一下就知道效果了。