ROS下利用realsense采集RGBD圖像合成點雲

摘要：在ROS kinetic下，利用realsense D435深度相機采集校准的RGBD圖片，合成點雲，在rviz中查看點雲，最后保存成pcd文件。

 

一、 各種bug

代碼編譯成功后，打開rviz添加pointcloud2選項卡，當我訂閱合成點雲時，可視化失敗，選項卡報錯：

1）Data size (9394656 bytes) does not match width (640) times height (480) times point_step (32).  Dropping message.

解讀：通過  rostopic echo /pointcloud_topic  讀取相機節點發布的原始點雲的相關數據，可以發現每一幀原始點雲的點數量為307200。合成點雲的點數量為  9394656 / 32 ,約26萬。可以猜測由於某種原因，系統把每一幀合成點雲的數據都丟棄了。

原因：我事先給定合成點雲的大小為，height = 480，width = 640. 然而在合成點雲的過程中，剔除了部分違法值(d=0)，由此導致合成點雲的點數量與指定的點數量不匹配，合成點雲的數據因此被丟棄。

解決方法：采用如下方法給定點雲大小， cloud->height = 1; cloud->width = cloud->points.size(); 

 

2）transform xxxxx；

解讀：通過  rostopic echo /pointcloud_topic  ，發現原始點雲數據具有如下信息，

header: 
  seq: 50114
  stamp: 
    secs: 1528439158
    nsecs: 557543003
  frame_id: "camera_color_optical_frame"

由此推斷，合成點雲缺失參考坐標系header.frame_id。點雲中點的XYZ屬性是相對於某個坐標系來描述的，因此，需要指定點雲的參考坐標系。

解決方法：添加點雲的header信息， 

pub_pointcloud.header.frame_id = "camera_color_optical_frame"; 
pub_pointcloud.header.stamp = ros::Time::now();

 

3）將合成的點雲存儲成pcd文件時遇到如下報錯：

[ INFO] [1528442016.931874649]: point cloud size = 0
terminate called after throwing an instance of 'pcl::IOException' what(): : [pcl::PCDWriter::writeASCII] Input point cloud has no data! Aborted (core dumped)

經過多方查找，摸索了一步trick，分享給大家。真實報錯原因仍未查明，期待前輩的指點。

高博的源代碼如下所示，里面的cloud是pcl的數據類型，

pcl::io::savePCDFile( "./pointcloud.pcd", *cloud );  。

我的源代碼如下面所示，先通過 pcl::toROSMsg() 將pcl的數據類型轉換成ros的數據類型，再寫入pcd中，即可跳過報錯。

 

4）相機內參

由於在彩色圖和深度圖配准的過程中，選用的是彩色圖的坐標系，因此在合成點雲（像素坐標在變換到相機坐標）時應該選用彩色圖的相機內參。

realsense官方提供了一個應用程序可以查看所有視頻流的內參。

gordon@gordon-5577:/usr/local/bin$ ./rs-sensor-control 

如下所示

84 : Color #0 (Video Stream: Y16 640x480@ 60Hz)
85 : Color #0 (Video Stream: BGRA8 640x480@ 60Hz)
86 : Color #0 (Video Stream: RGBA8 640x480@ 60Hz)
87 : Color #0 (Video Stream: BGR8 640x480@ 60Hz)
88 : Color #0 (Video Stream: RGB8 640x480@ 60Hz)
89 : Color #0 (Video Stream: YUYV 640x480@ 60Hz)
90 : Color #0 (Video Stream: Y16 640x480@ 30Hz)
91 : Color #0 (Video Stream: BGRA8 640x480@ 30Hz)
92 : Color #0 (Video Stream: RGBA8 640x480@ 30Hz)
93 : Color #0 (Video Stream: BGR8 640x480@ 30Hz)
94 : Color #0 (Video Stream: RGB8 640x480@ 30Hz)
95 : Color #0 (Video Stream: YUYV 640x480@ 30Hz)
96 : Color #0 (Video Stream: Y16 640x480@ 15Hz)
97 : Color #0 (Video Stream: BGRA8 640x480@ 15Hz)
98 : Color #0 (Video Stream: RGBA8 640x480@ 15Hz)
99 : Color #0 (Video Stream: BGR8 640x480@ 15Hz)
100: Color #0 (Video Stream: RGB8 640x480@ 15Hz)
101: Color #0 (Video Stream: YUYV 640x480@ 15Hz)
102: Color #0 (Video Stream: Y16 640x480@ 6Hz)
103: Color #0 (Video Stream: BGRA8 640x480@ 6Hz)
104: Color #0 (Video Stream: RGBA8 640x480@ 6Hz)
105: Color #0 (Video Stream: BGR8 640x480@ 6Hz)
106: Color #0 (Video Stream: RGB8 640x480@ 6Hz)
107: Color #0 (Video Stream: YUYV 640x480@ 6Hz)

 

5）深度圖從ROS的數據類型轉換為CV的數據類型

 參看鏈接

 

二、程序代碼

#include <ros/ros.h>
#include <image_transport/image_transport.h>
#include <opencv2/highgui/highgui.hpp>
#include <cv_bridge/cv_bridge.h>
#include <sensor_msgs/image_encodings.h>
#include <sensor_msgs/PointCloud2.h>

// PCL 庫
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <pcl/visualization/pcl_visualizer.h>
#include <pcl_conversions/pcl_conversions.h>
 
// 定義點雲類型
typedef pcl::PointCloud<pcl::PointXYZRGB> PointCloud; 

using namespace std;
//namespace enc = sensor_msgs::image_encodings;

// 相機內參
const double camera_factor = 1000;
const double camera_cx = 321.798;
const double camera_cy = 239.607;
const double camera_fx = 615.899;
const double camera_fy = 616.468;

// 全局變量：圖像矩陣和點雲
cv_bridge::CvImagePtr color_ptr, depth_ptr;
cv::Mat color_pic, depth_pic;

void color_Callback(const sensor_msgs::ImageConstPtr& color_msg)
{
  //cv_bridge::CvImagePtr color_ptr;
  try
  {
    cv::imshow("color_view", cv_bridge::toCvShare(color_msg, sensor_msgs::image_encodings::BGR8)->image);
    color_ptr = cv_bridge::toCvCopy(color_msg, sensor_msgs::image_encodings::BGR8);    

    cv::waitKey(1050); // 不斷刷新圖像，頻率時間為int delay，單位為ms
  }
  catch (cv_bridge::Exception& e )
  {
    ROS_ERROR("Could not convert from '%s' to 'bgr8'.", color_msg->encoding.c_str());
  }
  color_pic = color_ptr->image;

  // output some info about the rgb image in cv format
  cout<<"output some info about the rgb image in cv format"<<endl;
  cout<<"rows of the rgb image = "<<color_pic.rows<<endl; 
  cout<<"cols of the rgb image = "<<color_pic.cols<<endl; 
  cout<<"type of rgb_pic's element = "<<color_pic.type()<<endl; 
}


void depth_Callback(const sensor_msgs::ImageConstPtr& depth_msg)
{
  //cv_bridge::CvImagePtr depth_ptr;
  try
  {
    //cv::imshow("depth_view", cv_bridge::toCvShare(depth_msg, sensor_msgs::image_encodings::TYPE_16UC1)->image);
    //depth_ptr = cv_bridge::toCvCopy(depth_msg, sensor_msgs::image_encodings::TYPE_16UC1); 
    cv::imshow("depth_view", cv_bridge::toCvShare(depth_msg, sensor_msgs::image_encodings::TYPE_32FC1)->image);
    depth_ptr = cv_bridge::toCvCopy(depth_msg, sensor_msgs::image_encodings::TYPE_32FC1); 

    cv::waitKey(1050);
  }
  catch (cv_bridge::Exception& e)
  {
    ROS_ERROR("Could not convert from '%s' to 'mono16'.", depth_msg->encoding.c_str());
  }

  depth_pic = depth_ptr->image;

  // output some info about the depth image in cv format
  cout<<"output some info about the depth image in cv format"<<endl;
  cout<<"rows of the depth image = "<<depth_pic.rows<<endl; 
  cout<<"cols of the depth image = "<<depth_pic.cols<<endl; 
  cout<<"type of depth_pic's element = "<<depth_pic.type()<<endl; 
}
int main(int argc, char **argv)
{
  ros::init(argc, argv, "image_listener");
  ros::NodeHandle nh;
  cv::namedWindow("color_view");
  cv::namedWindow("depth_view");
  cv::startWindowThread();
  image_transport::ImageTransport it(nh);
  image_transport::Subscriber sub = it.subscribe("/camera/color/image_raw", 1, color_Callback);
  image_transport::Subscriber sub1 = it.subscribe("/camera/aligned_depth_to_color/image_raw", 1, depth_Callback);
  ros::Publisher pointcloud_publisher = nh.advertise<sensor_msgs::PointCloud2>("generated_pc", 1);
 // 點雲變量
  // 使用智能指針，創建一個空點雲。這種指針用完會自動釋放。
  PointCloud::Ptr cloud ( new PointCloud );
  sensor_msgs::PointCloud2 pub_pointcloud;

  double sample_rate = 1.0; // 1HZ，1秒發1次 
  ros::Rate naptime(sample_rate); // use to regulate loop rate 

  cout<<"depth value of depth map : "<<endl;

  while (ros::ok()) {
    // 遍歷深度圖
    for (int m = 0; m < depth_pic.rows; m++){
      for (int n = 0; n < depth_pic.cols; n++){
          // 獲取深度圖中(m,n)處的值
          float d = depth_pic.ptr<float>(m)[n];//ushort d = depth_pic.ptr<ushort>(m)[n];
          // d 可能沒有值，若如此，跳過此點
          if (d == 0)
             continue;
          // d 存在值，則向點雲增加一個點
          pcl::PointXYZRGB p;

          // 計算這個點的空間坐標
          p.z = double(d) / camera_factor;
          p.x = (n - camera_cx) * p.z / camera_fx;
          p.y = (m - camera_cy) * p.z / camera_fy;
            
          // 從rgb圖像中獲取它的顏色
          // rgb是三通道的BGR格式圖，所以按下面的順序獲取顏色
          p.b = color_pic.ptr<uchar>(m)[n*3];
          p.g = color_pic.ptr<uchar>(m)[n*3+1];
          p.r = color_pic.ptr<uchar>(m)[n*3+2];
        
          // 把p加入到點雲中
          cloud->points.push_back( p );
      }
    }
        

    // 設置並保存點雲
    cloud->height = 1;
    cloud->width = cloud->points.size();
    ROS_INFO("point cloud size = %d",cloud->width);
    cloud->is_dense = false;// 轉換點雲的數據類型並存儲成pcd文件
    pcl::toROSMsg(*cloud,pub_pointcloud);
    pub_pointcloud.header.frame_id = "camera_color_optical_frame";
    pub_pointcloud.header.stamp = ros::Time::now();
    pcl::io::savePCDFile("./pointcloud.pcd", pub_pointcloud);
    cout<<"publish point_cloud height = "<<pub_pointcloud.height<<endl;
    cout<<"publish point_cloud width = "<<pub_pointcloud.width<<endl;

    // 發布合成點雲和原始點雲
    pointcloud_publisher.publish(pub_pointcloud);
    ori_pointcloud_publisher.publish(cloud_msg);
    
    // 清除數據並退出
    cloud->points.clear();

    ros::spinOnce(); //allow data update from callback; 
    naptime.sleep(); // wait for remainder of specified period; 
  }

  cv::destroyWindow("color_view");
  cv::destroyWindow("depth_view");
}