PCL点云变换与移除NaN

#include <iostream>
#include <pcl/io/pcd_io.h>
#include <pcl/io/ply_io.h>
#include <pcl/point_cloud.h>
#include <pcl/console/parse.h>
#include <pcl/common/transforms.h>
#include <pcl/visualization/pcl_visualizer.h>
// 命令行的帮助提示
void showHelp(char * program_name)
{
 std::cout << std::endl;
 std::cout << "Usage: " << program_name << " cloud_filename.[pcd|ply]" << std::endl;  std::cout << "-h:  Show this help." << std::endl;
}
 
int main (int argc, char** argv)
{  
if (pcl::console::find_switch (argc, argv, "-h") || pcl::console::find_switch (argc, argv, "--help"))
 {
   showHelp (argv[0]);    return 0;
 }  
// 读取文件
 std::vector<int> filenames;  bool file_is_pcd = false;
 filenames = pcl::console::parse_file_extension_argument (argc, argv, ".ply");  if (filenames.size () != 1)  
{
   filenames = pcl::console::parse_file_extension_argument (argc, argv, ".pcd");    
if (filenames.size () != 1) 
{
     showHelp (argv[0]);    
      return -1;
   } 
else {
     file_is_pcd = true;
   }  } 
  
//载入文件
 pcl::PointCloud<pcl::PointXYZ>::Ptr source_cloud (new pcl::PointCloud<pcl::PointXYZ> ()); 
  if (file_is_pcd) 
 {   
  if (pcl::io::loadPCDFile (argv[filenames[0]], *source_cloud) < 0) 
 {
     std::cout << "Error loading point cloud " << argv[filenames[0]] << std::endl << std::endl;
     showHelp (argv[0]);   
       return -1;
   }
 } 
else {  
   if (pcl::io::loadPLYFile (argv[filenames[0]], *source_cloud) < 0)  {
     std::cout << "Error loading point cloud " << argv[filenames[0]] << std::endl << std::endl;
     showHelp (argv[0]);   
       return -1;
   }
 } 
 /* Reminder: how transformation matrices work :          |-------> This column is the translation
   | 1 0 0 x |  \
   | 0 1 0 y |   }-> The identity 3x3 matrix (no rotation) on the left
   | 0 0 1 z |  /
   | 0 0 0 1 |    -> We do not use this line (and it has to stay 0,0,0,1)   METHOD #1: Using a Matrix4f
   This is the "manual" method, perfect to understand but error prone !  */
 Eigen::Matrix4f transform_1 = Eigen::Matrix4f::Identity();  // Define a rotation matrix 定义旋转的角度  再有角度计算出旋转矩阵
 float theta = M_PI/4; // The angle of rotation in radians
 transform_1 (0,0) = cos (theta);
 transform_1 (0,1) = -sin(theta);
 transform_1 (1,0) = sin (theta);
 transform_1 (1,1) = cos (theta);  //    (row, column)  // Define a translation of 2.5 meters on the x axis.
 transform_1 (0,3) = 2.5;
//意思就是在第一行第四个元素的值为2.5，也就是在x轴的平移为2.5  
//   打印出这个变换矩阵
 printf ("Method #1: using a Matrix4f\n");
 std::cout << transform_1 << std::endl; 

/*  第二种方案 更简单的方案  */
 Eigen::Affine3f transform_2 = Eigen::Affine3f::Identity();  
 transform_2.translation() << 2.5, 0.0, 0.0;  
// The same rotation matrix as before; theta radians arround Z axis  transform_2.rotate (Eigen::AngleAxisf (theta, Eigen::Vector3f::UnitZ()));  // Print the transformation
 printf ("\nMethod #2: using an Affine3f\n");
 std::cout << transform_2.matrix() << std::endl;  
// Executing the transformationpcl::PointCloud<pcl::PointXYZ>::Ptr transformed_cloud (new pcl::PointCloud<pcl::PointXYZ> ()); 
 
// 你可以使用 transform_1 或者 transform_2;效果都是一样的 
 pcl::transformPointCloud (*source_cloud, *transformed_cloud, transform_2);  // 可视化的
 printf(  "\nPoint cloud colors :  white  = original point cloud\n"
"                        red  = transformed point cloud\n");
 pcl::visualization::PCLVisualizer viewer ("Matrix transformation example");   // 为点云设置RGB的值
 pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> source_cloud_color_handler (source_cloud, 255, 255, 255);  // We add the point cloud to the viewer and pass the color handler
 viewer.addPointCloud (source_cloud, source_cloud_color_handler, "original_cloud"); pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> transformed_cloud_color_handler (transformed_cloud, 230, 20, 20); // Red
 viewer.addPointCloud (transformed_cloud, transformed_cloud_color_handler, "transformed_cloud");  viewer.addCoordinateSystem (1.0, 0);
 viewer.setBackgroundColor(0.05, 0.05, 0.05, 0); //设置背景颜色
 viewer.setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 2, "original_cloud");
 viewer.setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 2, "transformed_cloud");  //viewer.setPosition(800, 400); // Setting visualiser window position
 while (!viewer.wasStopped ()) { // Display the visualiser until 'q' key is pressed    viewer.spinOnce ();
 }  return 0;
}

# .PCD v0.7 - Point Cloud Data file format
VERSION 0.7
FIELDS x y z rgba
SIZE 4 4 4 4
TYPE F F F U
COUNT 1 1 1 1
WIDTH 640
HEIGHT 480
VIEWPOINT 0 0 0 1 0 0 0
POINTS 307200
DATA ascii
nan nan nan 10135463
nan nan nan 10398635
nan nan nan 10070692
nan nan nan 10268071
...

#include <pcl/io/pcd_io.h>
#include <pcl/filters/filter.h>
#include <iostream>
#include <pcl/visualization/cloud_viewer.h>
int main(int argc,char** argv)
{if(argc !=3)
{
 std::cout <<"\tUsage: "<<argv[0] <<"<input cloud> <output cloud>" <<std::endl;  
return  -1;
}
pcl::PointCloud<pcl::PointXYZRGBA>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZRGBA>);

if(pcl::io::loadPCDFile<pcl::PointXYZRGBA>(argv[1],*cloud) !=0)
{ 
return -1;
}

//the mapping tells you to that points of the oldcloud the new ones correspond//but we  will not use it
std::vector<int> mapping;
pcl::removeNaNFromPointCloud(*cloud, *cloud, mapping);

//pcl::removeNaNFromPointCloud(*cloud, *cloud, mapping);//save it back
pcl::io::savePCDFileASCII(argv[2],*cloud);
pcl::visualization::CloudViewer viewer(argv[2]);
        viewer.showCloud(cloud);      
  while (!viewer.wasStopped())
   {        
// Do nothing but wait.   
    }
}