pcl之滤波器（一）

pcl滤波器

pcl一共是有十二个主要模块，详细了解可以查看官网。https://pcl.readthedocs.io/projects/tutorials/en/latest/#basic-usage

今天学习一下pcl的滤波器模块。

滤波器模块，官网一共是提供了6个例程，今天先来看第一第二个。

直通滤波器

主要使用的API是 passthrough

#include <iostream>
#include <pcl/point_types.h>
#include <pcl/filters/passthrough.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
    cloud->width = 50;    //点数量
    cloud->height = 1;		
    cloud->points.resize(cloud->width * cloud->height);

	//遍历点赋值，值为0-1024之间的随机浮点值
    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);
    }

    std::cerr << "Cloud before filtering: " << std::endl;
    for (const auto& point : *cloud)
        std::cerr << "    " << point.x << " "
        << point.y << " "
        << point.z << std::endl;

    // Create the filtering object
    pcl::PassThrough<pcl::PointXYZ> pass;
    pass.setInputCloud(cloud);
    pass.setFilterFieldName("z");     //设置z为过滤因子
    pass.setFilterLimits(0.0, 200.0);	//只通过z轴值为0-200之间的点
    pass.filter(*cloud_filtered);

    std::cerr << "Cloud after filtering: " << std::endl;
    for (const auto& point : *cloud_filtered)
        std::cerr << "    " << point.x << " "
        << point.y << " "
        << point.z << std::endl;

    system("pause");
    return (0);
}

CMakeLists.txt

cmake_minimum_required(VERSION 3.5 FATAL_ERROR)

project(passthrough)

find_package(PCL 1.2 REQUIRED)

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

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

体素滤波器降采样

即使用体素化网格方法，减少一个点云数据集中点的数量。

VoxelGrid 类在输入点云数据上创建一个3D体素网格(将体素网格想象为空间中的一组微小的3D盒子)。然后，在每个体素(即3D框)中，所有存在的点都将用它们的质心进行近似(即下采样)。这种方法比用体素的中心逼近它们要慢一些，但它更准确地表示底层表面。

感兴趣的可以去看看YouTube上的这个视频
https://youtu.be/YHR6_OIxtFI?t=24

程序中使用的pcd文件地址
https://raw.github.com/PointCloudLibrary/data/master/tutorials/table_scene_lms400.pcd

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

int
main ()
{
  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) << ")." << std::endl;

  //创建一个voxel叶大小为1cm的pcl::VoxelGrid滤波器，
  pcl::VoxelGrid<pcl::PCLPointCloud2> sor;  //创建滤波对象
  sor.setInputCloud (cloud);            //设置需要过滤的点云给滤波对象
  sor.setLeafSize (0.01f, 0.01f, 0.01f);  //设置滤波时创建的体素体积为1cm的立方体
  sor.filter (*cloud_filtered);           //执行滤波处理，存储输出

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

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

  return (0);
}

代码还是比较简单的，先看一下结果吧

点少了十倍。

视觉效果大致如下

放大看效果比较明显一点

CMakeLists.txt

cmake_minimum_required(VERSION 3.5 FATAL_ERROR)

project(voxel_grid)

find_package(PCL 1.2 REQUIRED)

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

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