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);

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);
}



int
main (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);