PCL点云处理_点云滤波（3）

经典的滤波方法有：直通滤波器，体素滤波器，统计滤波器，条件滤波，半径滤波器，双边滤波，高斯滤波

直通滤波
代码：

#include <iostream>
#include <ctime>
#include <pcl/point_types.h>
#include <pcl/filters/passthrough.h>
int main (int argc, char** argv)
{ 
   //种下时间种子数，用来产生不同的随机数
  srand(time(0));
  //创建点云
  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);
  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered (new pcl::PointCloud<pcl::PointXYZ>);
  //填入点云数据
  cloud->width  = 5;
  cloud->height = 1;
  //修改点云的大小
  cloud->points.resize (cloud->width * cloud->height);
  //建立3组随机数
  for (size_t i = 0; i < cloud->points.size (); ++i)
  {
    cloud->points[i].x = rand () / (RAND_MAX + 1.0f)-0.5;
    cloud->points[i].y = rand () / (RAND_MAX + 1.0f)-0.5;
    cloud->points[i].z = rand () / (RAND_MAX + 1.0f)-0.5;
  }
  //输出点云坐标
  std::cerr << "Cloud before filtering: " << std::endl;
  for (size_t i = 0; i < cloud->points.size (); ++i)
    std::cerr << "    " << cloud->points[i].x << " " 
                        << cloud->points[i].y << " " 
                        << cloud->points[i].z << std::endl;

  /*-------------------直通滤波-------------------------------*/
  // 创建滤波器对象
  pcl::PassThrough<pcl::PointXYZ> pass;
  //设置输入的点云
  pass.setInputCloud (cloud);
  //滤波字段名被设置为X轴方向
  pass.setFilterFieldName ("x");
  //设置滤波范围
  pass.setFilterLimits (0.0, 1.0);
  //pass.setFilterLimitsNegative (true);
  //执行滤波，保存结果到cloud_filtered
  pass.filter (*cloud_filtered);
  //输出滤波后点云数据
  std::cerr << "Cloud after filtering: " << std::endl;
  for (size_t i = 0; i < cloud_filtered->points.size (); ++i)
    std::cerr << "    " << cloud_filtered->points[i].x << " " 
                        << cloud_filtered->points[i].y << " " 
                        << cloud_filtered->points[i].z << std::endl;
  return (0);
}

调试数据

体素滤波器（下采样）
代码：

#include <iostream>
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <pcl/filters/voxel_grid.h>
#include <pcl/visualization/range_image_visualizer.h> //深度图可视化
#include<pcl/visualization/cloud_viewer.h>
int main (int argc, char** argv)
{
      pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);
      pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered(new pcl::PointCloud<pcl::PointXYZ>);
      // 填入点云数据
      pcl::PCDReader reader;
      // 把路径改为自己存放文件的路径
      reader.read ("C:\\Users\\Empower\\Desktop\\exep\\bunny.pcd", *cloud);
      std::cerr << "PointCloud before filtering: " << cloud->width * cloud->height 
      << " data points (" << pcl::getFieldsList (*cloud) << ").";
      // 创建滤波器对象 体素网格下采样
      pcl::VoxelGrid<pcl::PointXYZ> sor;
      //设置输入的点云
      sor.setInputCloud (cloud);
      //点云降采样
      sor.setLeafSize (0.01f, 0.01f, 0.01f);
      //执行滤波，保存结果到cloud_filtered
      sor.filter (*cloud_filtered);
      std::cerr << "PointCloud after filtering: " << cloud_filtered->width * cloud_filtered->height 
           << " data points (" << pcl::getFieldsList (*cloud_filtered) << ").";
      pcl::PCDWriter writer;
      cout << "sta" << endl;
      //写入文件
      writer.write ("2d.pcd", *cloud_filtered, false);
      pcl::visualization::CloudViewer viewer1("ddd");//创建点云对象
      viewer1.showCloud (cloud_filtered);//显示点云
      cout << "ok" << endl;
      system("pause");
      return (0);
}

调试结果

统计滤波器
代码

 #include <iostream>
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <pcl/filters/statistical_outlier_removal.h>
#include <pcl/visualization/range_image_visualizer.h> //深度图可视化
#include<pcl/visualization/cloud_viewer.h>
int
main (int argc, char** argv)
{
	  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);
	  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered (new pcl::PointCloud<pcl::PointXYZ>);
	  // 填入点云数据
	  pcl::PCDReader reader;
	  reader.read<pcl::PointXYZ> ("C:\\Users\\Empower\\Desktop\\exep\\bunny.pcd", *cloud);
	  std::cerr << "Cloud before filtering: " << std::endl;
	  std::cerr << *cloud << std::endl;
	  // 创建滤波器对象 统计滤波器
	  pcl::StatisticalOutlierRemoval<pcl::PointXYZ> sor;
	  //设置输入的点云
	  sor.setInputCloud (cloud);
	  //设置考虑查询点临近点数
	  sor.setMeanK (50);
	  //设置判断是否为离群点的阀值
	  sor.setStddevMulThresh (1.0);
	  //执行滤波，保存结果到cloud_filtered
	  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);
	 // 然后，使用同样的参数再次调用该滤波器，但是利用函数 setNegative 设置使输出取外点，以获取离群点数据 也就是原本滤除掉的点 。
	  sor.setNegative (true);
	  //执行滤波，保存结果到cloud_filtered
	  sor.filter (*cloud_filtered);
	  writer.write<pcl::PointXYZ> ("table_scene_lms400_outliers.pcd", *cloud_filtered, false);
	  pcl::visualization::CloudViewer viewer1("ddd");//创建点云对象
	  viewer1.showCloud(cloud_filtered);//显示点云
	  return (0);
}

调试结果

投影滤波：
代码

#include <iostream>
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <pcl/ModelCoefficients.h>
#include <pcl/filters/project_inliers.h>
int
 main (int argc, char** argv)
{
  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);
  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_projected (new pcl::PointCloud<pcl::PointXYZ>);
  // 填入点云数据
  cloud->width  = 5;
  cloud->height = 1;
  cloud->points.resize (cloud->width * cloud->height);
  for (size_t i = 0; i < cloud->points.size (); ++i)
  {
    cloud->points[i].x = 1024 * rand () / (RAND_MAX + 1.0f);
    cloud->points[i].y = 1024 * rand () / (RAND_MAX + 1.0f);
    cloud->points[i].z = 1024 * rand () / (RAND_MAX + 1.0f);
  }
  std::cerr << "Cloud before projection: " << std::endl;
  for (size_t i = 0; i < cloud->points.size (); ++i)
    std::cerr << "    " << cloud->points[i].x << " " 
                        << cloud->points[i].y << " " 
                        << cloud->points[i].z << std::endl;
  // 创建一个系数为X=Y=0,Z=1的平面
  pcl::ModelCoefficients::Ptr coefficients (new pcl::ModelCoefficients ());
  //设置四个数学模型系数
  coefficients->values.resize (4);
  coefficients->values[0] = coefficients->values[1] = 0;
  coefficients->values[2] = 1.0;
  coefficients->values[3] = 0;
  // 创建滤波器对象 投影滤波
  pcl::ProjectInliers<pcl::PointXYZ> proj;
  //设置投影滤波模型（平面）
  proj.setModelType (pcl::SACMODEL_PLANE);
  //是指输入点云
  proj.setInputCloud (cloud);
  //设置模型对应的系数
  proj.setModelCoefficients (coefficients);
  //投影结果存储
  proj.filter (*cloud_projected);
  //输出投影结果
  std::cerr << "Cloud after projection: " << std::endl;
  for (size_t i = 0; i < cloud_projected->points.size (); ++i)
    std::cerr << "    " << cloud_projected->points[i].x << " " 
                        << cloud_projected->points[i].y << " " 
                        << cloud_projected->points[i].z << std::endl;

  return (0);
}

调试结果：