ROS机器人Diego制作9-ROS视觉系统之LeTV Xtion

ROS机器人Diego制作9-ROS视觉系统之LeTV Xtion

说明：

• 介绍接入LeTV Xtion深度相机
• 机器人的SLAM自适应导航，最基本的要有激光雷达数据或者点云数据
• 但激光雷达目前价格太高，另外可替代的方法是用具有深度摄像机作为传感器发布点云数据
• 一般用的比较多的是微软的Kinect，或者华硕的Xtion。
• 目前Kinect已经有2.0版本，但Kinect2.0支持的USB3.0接口，树莓派USB接口都是2.0的
• 基于价格和体积的考虑，选择LeTV Xtion ，效果还不错。

效果图：

安装方法

• 安装OpenNI包

sudo apt-get install ros-kinetic-openni-camera
sudo apt-get install ros-kinetic-openni-launch

• 安装Xtion的新版驱动（现在买到的都是新版本的）

sudo apt-get install libopenni-sensor-primesense0

• 启动openni节点（先要在其他终端中启动roscore）

roslaunch openni_launch openni.launch

• 启动成功后终端应该显示如下信息
  

• 这里的警告信息可以忽略，不影响使用 ,主要是标定没做

• 查看摄像头的所生成的影像

rosrun image_view disparity_view image:=/camera/depth/disparity 

• 也可以通过rviz来查看生成的影像，执行如下命令

rosrun rviz rviz

生成点云数据

• 参考了两篇文档
• OpenNI本身就已经有点云数据了，这篇文章完全是看了前辈的文章，就想把这些优秀的代码整合到ROS中来
• 官方文档http://wiki.ros.org/navigation/Tutorials/RobotSetup/Sensors
• 古月居的http://blog.csdn.net/hcx25909/article/details/8654684
• 源代码：

#include <ros/ros.h>
#include <sensor_msgs/PointCloud.h>
#include <XnCppWrapper.h>
#include <iostream>
#include <iomanip>
#include <vector>

using namespace xn; 
using namespace std; 

struct SColorPoint3D  
{  
    float  X;  
    float  Y;  
    float  Z;  
    float  R;  
    float  G;  
    float  B;  

    SColorPoint3D( XnPoint3D pos, XnRGB24Pixel color )  
    {  
      X = pos.X;  
      Y = pos.Y;  
      Z = pos.Z;  
      R = (float)color.nRed / 255;  
      G = (float)color.nGreen / 255;  
      B = (float)color.nBlue / 255;  
    }  
};  

void GeneratePointCloud( DepthGenerator& rDepthGen,  
                         const XnDepthPixel* pDepth,  
                         const XnRGB24Pixel* pImage,  
                         vector<SColorPoint3D>& vPointCloud )  
{  
    // number of point is the number of 2D image pixel  
    DepthMetaData mDepthMD;  
    rDepthGen.GetMetaData( mDepthMD );  
    unsigned int uPointNum = mDepthMD.FullXRes() * mDepthMD.FullYRes();  

    // build the data structure for convert  
    XnPoint3D* pDepthPointSet = new XnPoint3D[ uPointNum ];  
    unsigned int i, j, idxShift, idx;  
    for( j = 0; j < mDepthMD.FullYRes(); ++j )  
    {  
        idxShift = j * mDepthMD.FullXRes();  
        for( i = 0; i < mDepthMD.FullXRes(); ++i )  
        {  
            idx = idxShift + i;  
            pDepthPointSet[idx].X = i;  
            pDepthPointSet[idx].Y = j;  
            pDepthPointSet[idx].Z = pDepth[idx];  
        }  
    }  

    // un-project points to real world  
    XnPoint3D* p3DPointSet = new XnPoint3D[ uPointNum ];  
    rDepthGen.ConvertProjectiveToRealWorld( uPointNum, pDepthPointSet, p3DPointSet );  
    delete[] pDepthPointSet;  

    // build point cloud  
    for( i = 0; i < uPointNum; ++ i )  
    {  
        // skip the depth 0 points  
        if( p3DPointSet[i].Z == 0 )  
            continue;  

        vPointCloud.push_back( SColorPoint3D( p3DPointSet[i], pImage[i] ) );  
    }  
    delete[] p3DPointSet;  
}  


int main(int argc, char** argv){
  ros::init(argc, argv, "point_cloud_publisher");

  ros::NodeHandle n;
  ros::Publisher cloud_pub = n.advertise<sensor_msgs::PointCloud>("cloud", 50);

  unsigned int num_points = 100;

  int count = 0;
  ros::Rate r(1.0);

  /////////////////
  XnStatus eResult = XN_STATUS_OK;  
  int i = 0;  

  // init  
  Context mContext;  
  eResult = mContext.Init();    

  DepthGenerator mDepthGenerator;  
  eResult = mDepthGenerator.Create(mContext);  
  ImageGenerator mImageGenerator;  
  eResult = mImageGenerator.Create(mContext);  

  // set output mode  
  XnMapOutputMode mapMode;  
  mapMode.nXRes = XN_VGA_X_RES;  
  mapMode.nYRes = XN_VGA_Y_RES;  
  mapMode.nFPS  = 30;  
  eResult = mDepthGenerator.SetMapOutputMode(mapMode);  
  eResult = mImageGenerator.SetMapOutputMode(mapMode);  

  // start generating    
  eResult = mContext.StartGeneratingAll();  
  // read data  
  vector<SColorPoint3D> vPointCloud; 
  /////////////////


  while(n.ok()){

    eResult = mContext.WaitNoneUpdateAll();  
    // get the depth map  
    const XnDepthPixel*  pDepthMap = mDepthGenerator.GetDepthMap();  

    // get the image map  
    const XnRGB24Pixel*  pImageMap = mImageGenerator.GetRGB24ImageMap();  

    // generate point cloud  
    vPointCloud.clear();  
    GeneratePointCloud(mDepthGenerator, pDepthMap, pImageMap, vPointCloud );  

    // print point cloud  
    cout.flags(ios::left);    //Left-aligned  
    cout << "Point number: " << vPointCloud.size() << endl; 

    num_points=vPointCloud.size();

    sensor_msgs::PointCloud cloud;
    cloud.header.stamp = ros::Time::now();
    cloud.header.frame_id = "sensor_frame";

    cloud.points.resize(num_points);

    //we'll also add an intensity channel to the cloud
    cloud.channels.resize(3);
    cloud.channels[0].name = "R";
    cloud.channels[0].values.resize(num_points);
    cloud.channels[1].name = "G";
    cloud.channels[1].values.resize(num_points);
    cloud.channels[2].name = "G";
    cloud.channels[2].values.resize(num_points);

    //generate some fake data for our point cloud
    for(unsigned int i = 0; i < num_points; ++i){
      cloud.points[i].x = vPointCloud[i].X;
      cloud.points[i].y = vPointCloud[i].Y;
      cloud.points[i].z = vPointCloud[i].Z;
      cloud.channels[0].values[i] = vPointCloud[i].R;
      cloud.channels[1].values[i] = vPointCloud[i].G;
      cloud.channels[2].values[i] = vPointCloud[i].B;
    }

    cloud_pub.publish(cloud);
    ++count;
    r.sleep();
  }
  return 0;
}

• 另外在包目录下的CMakeLists.txt文件中有两处修改，否则编译会出错
• 增加openni的引用路径

include_directories ("/usr/include/ni/")

• 增加新的可执行文件说明

add_executable(XtionPointCloud src/XtionPointCloud.cpp)
target_link_libraries(XtionPointCloud ${catkin_LIBRARIES})
target_link_libraries(XtionPointCloud OpenNI)

• 修改保存后在~/catkin_ws下执行编译命令

catkin_make

测试

• 新终端，启动roscore

roscore

• 新终端，启动XtionPointCloud节点

rosrun diego_nav XtionPointCloud

• 新终端，查看发布的点云数据

rostopic echo /cloud

• 效果图：

• 树莓派处理其点云数据还是很吃力的，这个时候树莓派的系统资源使用情况：