PCL滤波介绍（2）

#include <iostream>
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <pcl/filters/statistical_outlier_removal.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>);  
// 定义读取对象  
pcl::PCDReader reader;  // 读取点云文件reader.read<pcl::PointXYZ> ("table_scene_lms400.pcd", *cloud); std::cerr << "Cloud before filtering: " << std::endl;
 std::cerr << *cloud << std::endl;  
// 创建滤波器，对每个点分析的临近点的个数设置为50 ，并将标准差的倍数设置为1  这意味着如果一个点的距离超出了平均距离一个标准差以上，则该点被标记为离群点，并将它移除，存储起来
 pcl::StatisticalOutlierRemoval<pcl::PointXYZ> sor;   //创建滤波器对象
 sor.setInputCloud (cloud);                           //设置待滤波的点云
 sor.setMeanK (50);                 //设置在进行统计时考虑查询点临近点数
 sor.setStddevMulThresh (1.0);        //设置判断是否为离群点的阀值
 sor.filter (*cloud_filtered);                    //存储
 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);
}

#include <iostream>
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <pcl/ModelCoefficients.h>             //模型系数头文件
#include <pcl/filters/project_inliers.h>          //投影滤波类头文件
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>);  //创建点云并打印出来
 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;  
 // 填充ModelCoefficients的值,使用ax+by+cz+d=0平面模型，其中 a=b=d=0,c=1 也就是X——Y平面  //定义模型系数对象，并填充对应的数据
 pcl::ModelCoefficients::Ptr coefficients (new pcl::ModelCoefficients ());
 coefficients->values.resize (4);
 coefficients->values[0] = coefficients->values[1] = 0;
 coefficients->values[2] = 1.0;
 coefficients->values[3] = 0;  
 // 创建ProjectInliers对象，使用ModelCoefficients作为投影对象的模型参数
 pcl::ProjectInliers<pcl::PointXYZ> proj;     //创建投影滤波对象
 proj.setModelType (pcl::SACMODEL_PLANE);      //设置对象对应的投影模型
 proj.setInputCloud (cloud);                   //设置输入点云
 proj.setModelCoefficients (coefficients);       //设置模型对应的系数
 proj.filter (*cloud_projected);                 //投影结果存储
 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);
}