PCL中点云数据格式之间的转化

pcl::PointXYZ::PointXYZ ( float_x,
                 float_y,
                 float_z
                   ) 

 struct PCLPointCloud2 {
 PCLPointCloud2 () : header (), height (0), width (0), fields (),
is_bigendian (false), point_step (0), row_step (0),
 data (), is_dense (false)
 { #if defined(BOOST_BIG_ENDIAN)
 is_bigendian = true; #elif defined(BOOST_LITTLE_ENDIAN)
 is_bigendian = false; #else
#error "unable to determine system endianness" #endif
 }
 ::pcl::PCLHeader header;
 pcl::uint32_t height;
 pcl::uint32_t width;  std::vector< ::pcl::PCLPointField> fields;
 pcl::uint8_t is_bigendian;
 pcl::uint32_t point_step;
 pcl::uint32_t row_step;
 std::vector<pcl::uint8_t> data;
 pcl::uint8_t is_dense;
 public:
 typedef boost::shared_ptr< ::pcl::PCLPointCloud2> Ptr;
 typedef boost::shared_ptr< ::pcl::PCLPointCloud2 const> ConstPtr;
 }; // struct PCLPointCloud2

 pcl::PCLPointCloud2::Ptr cloud_blob (new pcl::PCLPointCloud2), cloud_filtered_blob (new pcl::PCLPointCloud2);//申明滤波前后的点云

  pcl::PointCloud<pcl::PointXYZ>::Ptr 
  cloud_filtered (new pcl::PointCloud<pcl::PointXYZ>), 
  cloud_p (new pcl::PointCloud<pcl::PointXYZ>), 
  cloud_f (new pcl::PointCloud<pcl::PointXYZ>);  

  // 读取PCD文件 
pcl::PCDReader reader;
reader.read ("table_scene_lms400.pcd", *cloud_blob);  
 //统计滤波前的点云个数
std::cerr << "PointCloud before filtering: " << cloud_blob->width * cloud_blob->height << " data points." << std::endl;  
// 创建体素栅格下采样:下采样的大小为1cm
 pcl::VoxelGrid<pcl::PCLPointCloud2> sor;  //体素栅格下采样对象
 sor.setInputCloud (cloud_blob);             //原始点云
 sor.setLeafSize (0.01f, 0.01f, 0.01f);    // 设置采样体素大小
 sor.filter (*cloud_filtered_blob);        //保存  
// 转换为模板点云
 pcl::fromPCLPointCloud2 (*cloud_filtered_blob, *cloud_filtered);

 std::cerr << "PointCloud after filtering: " << cloud_filtered->width * cloud_filtered->height << " data points." << std::endl;  
 
// 保存下采样后的点云  
  pcl::PCDWriter writer;
 writer.write<pcl::PointXYZ> ("table_scene_lms400_downsampled.pcd", *cloud_filtered, false);

cloud_filtered_blob 声明的数据格式为pcl::PCLPointCloud2::Ptr  cloud_filtered_blob (new pcl::PCLPointCloud2);

cloud_filtered 申明的数据格式  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered (new pcl::PointCloud<pcl::PointXYZ>)

MsgFieldMap field_map;
createMapping<PointT> (msg.fields, field_map);

/************************************************
关于如何使用PCL在ROS 中，实现简单的数据转化
时间：2017.3.31**/
#include <ros/ros.h>
// PCL specific includes
#include <sensor_msgs/PointCloud2.h>
#include <pcl_conversions/pcl_conversions.h>
#include <pcl/point_cloud.h>
#include <pcl/point_types.h>
#include <pcl/visualization/pcl_visualizer.h>
#include <boost/thread/thread.hpp>
#include <pcl/visualization/cloud_viewer.h>

ros::Publisher pub;
pcl::visualization::CloudViewer viewer("Cloud Viewer");

void cloud_cb (const sensor_msgs::PointCloud2ConstPtr& input)
{ // 创建一个输出的数据格式
 sensor_msgs::PointCloud2 output;  //ROS中点云的数据格式  
//对数据进行处理
pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZRGB>); output = *input;
 pcl::fromROSMsg(output,*cloud);     
//blocks until the cloud is actually rendered   
 viewer.showCloud(cloud);
 pub.publish (output);
}
    
intmain (int argc, char** argv)
{  // Initialize ROS
 ros::init (argc, argv, "my_pcl_tutorial");
 ros::NodeHandle nh;  

// Create a ROS subscriber for the input point cloud
 ros::Subscriber sub = nh.subscribe ("input", 1, cloud_cb);
  ros::Rate loop_rate(100);  
// Create a ROS publisher for the output point cloud
 pub = nh.advertise<sensor_msgs::PointCloud2> ("output", 1);    
 // Spin  
  ros::spin ();
/*while (!viewer.wasStopped ())
   {
   }
*/
}

  pcl::PCDWriter writer;
 writer.write<pcl::PointXYZ> ("ros_to_PCL.pcd", *cloud, false);