说明:

  • 将kitti数据集中 雷达点云图像投影到camera图像平面,
  • 并生成 深度图的灰度图(灰度值=深度x256 保存成int16位图像(kitti中 depth benchmark的做法))

输入:

  • P_rect_02: camera02相机内参
  • R_rect_00: 3x3 纠正旋转矩阵(使图像平面共面)(kitti特有的)

  • Tr_velo_to_cam: 激光雷达到camera00的变换矩阵

输出:

  • 投影图
  • 深度图的灰度图

坐标系:

变换公式:

以下等式说明了如何使用齐次坐标在相机0的图像平面上将空间中的3D激光雷达点X投影到2D像素点Y(使用Kitti自述文件中的表示法):

  • RT_velo_to_cam _ x :是将Velodyne坐标中的点x投影到编号为0的相机(参考相机)坐标系中
  • R_rect00 _RT_velo_to_cam _ x :是将Velodyne坐标中的点x投影到编号为0的相机(参考相机)坐标系中, 再以参考相机0为基础进行图像共面对齐修正(这是使用KITTI数据集的进行3D投影的必要操作)
  • P_rect_00 _ R_rect00 _RT_velo_to_cam _ x :是将Velodyne坐标中的点x投影到编号为0的相机(参考相机)坐标系中, 再进行图像共面对齐修正, 然后投影到相机0的像素坐标系中. 如果将P_rect_00改成P_rect_2, 也就是从参考相机0投影到相机2的像素坐标系中(其他相机相对与相机0有偏移b(i)).
  • 原始论文: Vision meets Robotics: The KITTI Dataset

代码:


  1. # -*- coding: utf-8 -*-
     
    #  数据来源: calib_cam_to_cam.txt 
    #  下载链接: http://www.cvlibs.net/datasets/kitti/raw_data.php?type=road  >  2011_10_03_drive_0047  >  [calibration]
    # R_rect_00: 9.999454e-01 7.259129e-03 -7.519551e-03 -7.292213e-03 9.999638e-01 -4.381729e-03 7.487471e-03 4.436324e-03 9.999621e-01
    ## P_rect_00: 7.188560e+02 0.000000e+00 6.071928e+02 0.000000e+00 0.000000e+00 7.188560e+02 1.852157e+02 0.000000e+00 0.000000e+00 0.000000e+00 1.000000e+00 0.000000e+00
    # ...
    ## R_rect_02: 9.999191e-01 1.228161e-02 -3.316013e-03 -1.228209e-02 9.999246e-01 -1.245511e-04 3.314233e-03 1.652686e-04 9.999945e-01
    # P_rect_02: 7.188560e+02 0.000000e+00 6.071928e+02 4.538225e+01 0.000000e+00 7.188560e+02 1.852157e+02 -1.130887e-01 0.000000e+00 0.000000e+00 1.000000e+00 3.779761e-03
     
    #  数据来源: calib_velo_to_cam.txt
    #  下载链接: http://www.cvlibs.net/datasets/kitti/raw_data.php?type=road  >  2011_10_03_drive_0047  >  [calibration]
    #   calib_time: 15-Mar-2012 11:45:23
    #   R: 7.967514e-03 -9.999679e-01 -8.462264e-04 -2.771053e-03 8.241710e-04 -9.999958e-01 9.999644e-01 7.969825e-03 -2.764397e-03
    #   T: -1.377769e-02 -5.542117e-02 -2.918589e-01
     
     
    # # png bin来源
    # data_odometry_color/dataset/sequences/00/image_2
    # data_odometry_velodyne/dataset/sequences/00/velodyne
     
    import sys
    import matplotlib.pyplot as plt
    import matplotlib.image as mpimg
    import numpy as np
    import utils
    from PIL import Image
    import math
     
     
    #-----------------------------------相机02内参矩阵-----------------------------------
    P_rect_02 = np.array( [ 7.188560000000e+02, 0.000000000000e+00, 6.071928000000e+02, 4.538225000000e+01, 
                            0.000000000000e+00,7.188560000000e+02, 1.852157000000e+02, -1.130887000000e-01,
                            0.000000000000e+00, 0.000000000000e+00, 1.000000000000e+00, 3.779761000000e-03]).reshape((3,4))
     
    R_rect_00 = np.array( [ 9.999454e-01, 7.259129e-03, -7.519551e-03,
                            -7.292213e-03, 9.999638e-01, -4.381729e-03, 
                            7.487471e-03, 4.436324e-03, 9.999621e-01]).reshape((3,3))
     
    # R_rect_02 = np.array( [ 9.999191e-01, 1.228161e-02 -3,.316013e-03,
    #                         -1.228209e-02, 9.999246e-01, -1.245511e-04, 
    #                         3.314233e-03, 1.652686e-04, 9.999945e-01]).reshape((3,3))
     
     
    #velo激光雷达 到 相机00(此处已知条件重点注意) 的变换矩阵
    Tr_velo_to_cam = np.array( [    7.967514e-03, -9.999679e-01, -8.462264e-04, -1.377769e-02,
                                    -2.771053e-03, 8.241710e-04, -9.999958e-01, -5.542117e-02,
                                    9.999644e-01, 7.969825e-03, -2.764397e-03, -2.918589e-01]).reshape((3,4))    
     
     
    #-----------------------------------数据文件位置---------------------------------------
    velo_files = "./data/00_velodyne/000005.bin"
    rgbimg = "./data/00_image_02/000005.png"
    resultImg = "./data/result_merge.png"
     
    data = {}
    data['P_rect_20'] = P_rect_02
    # Compute the velodyne to rectified camera coordinate transforms
    data['T_cam0_velo'] = Tr_velo_to_cam
    data['T_cam0_velo'] = np.vstack([data['T_cam0_velo'], [0, 0, 0, 1]])
     
    # pattern1:
    R_rect_00 = np.insert(R_rect_00,3,values=[0,0,0],axis=0)
    R_rect_00 = np.insert(R_rect_00,3,values=[0,0,0,1],axis=1)
    data['T_cam2_velo'] = R_rect_00.dot(data['T_cam0_velo']) #雷达 到 相机02的变换矩阵
    print(data['T_cam2_velo'])
     
    pointCloud = utils.load_velo_scan(velo_files)   #读取lidar原始数据
    points = pointCloud[:, 0:3]                                           # 获取 lidar xyz (front, left, up)
    points_homo = np.insert(points,3,1,axis=1).T    # 齐次化,并转置(一列表示一个点(x,y,z,1), 多少列就有多少个点)
    points_homo = np.delete(points_homo,np.where(points_homo[0,:]<0),axis=1) #以列为基准, 删除深度x=0的点
     
    proj_lidar = data['P_rect_20'].dot( data['T_cam2_velo'] ).dot(points_homo)  #相机坐标系3D点=相机02内参*激光雷达到相机02的变换矩阵*激光雷达3D点
    cam = np.delete(proj_lidar,np.where(proj_lidar[2,:]<0),axis=1)  #以列为基准, 删除投影图像点中深度z<0(在投影图像后方)的点 #3xN
    cam[:2,:] /= cam[2,:]   # 等价写法 cam[:2] /= cam[2] # 前两行元素分布除以第三行元素(归一化到相机坐标系z=1平面)(x=x/z, y =y/z)
     
     
    # -----------------------------------将激光投影点绘制到图像平面:绘制原图------------------------------------
    png = mpimg.imread(rgbimg)
    IMG_H,IMG_W,_ = png.shape
    plt.figure(figsize=((IMG_W)/72.0,(IMG_H)/72.0),dpi=72.0, tight_layout=True)
    # restrict canvas in range
    plt.axis([0,IMG_W,IMG_H,0])
    # plt.axis('off') 
    plt.imshow(png) #在画布上画出原图
     
    # filter point out of canvas
    u,v,z = cam
    u_out = np.logical_or(u<0, u>IMG_W)
    v_out = np.logical_or(v<0, v>IMG_H)
    outlier = np.logical_or(u_out, v_out)
    cam = np.delete(cam,np.where(outlier),axis=1)
    # generate color map from depth
    u,v,z = cam
     
    # 将激光投影点绘制到图像平面:绘制激光深度散点图
    plt.scatter([u],[v],c=[z],cmap='rainbow_r',alpha=0.5,s=1)
    plt.title("projection")
    plt.savefig(resultImg,bbox_inches='tight')
    plt.show()  #在画布上画出散点雷达深度投影
     
     
    #-----------------------------------单独保存深度图像成灰度图像---------------------------------------------------
    image_array = np.zeros((IMG_H, IMG_W), dtype=np.int16)
    for i in range(cam.shape[1]):
        x = int(round(u[i]))
        y = int(round(v[i]))
     
        # x = math.ceil(u[i]) #向上取整
        # y = math.ceil(v[i])
     
        depth =  int(z[i]*256)
        if 0<x<image_array.shape[1] and 0<y<image_array.shape[0]:
            image_array[y,x] = depth
     
    image_pil = Image.fromarray(image_array, 'I;16')   
    image_pil.save("result_16.png")
     
     
     
    print("done")

结果:

一帧激光点云+一张RGB图像得到彩色点云以及深度图

代码


    1. #include <iostream>
      #include <string>
      #include <opencv2/opencv.hpp>
      #include <pcl/io/pcd_io.h>
      #include <pcl/point_types.h>
      #include <pcl/common/transforms.h>
      #include <pcl/visualization/pcl_visualizer.h>
       
      typedef pcl::PointXYZRGBA PointT;
      typedef pcl::PointCloud<PointT> PointCloud;
      using namespace std;
       
       
      PointCloud::Ptr TransformBinFile2Points(const std::string &binFile, const cv::Mat &imRGB,
                              const cv::Mat &Tcl, cv::Mat &imDepth);
       
      PointCloud::Ptr TransformBinFile2PointsKITTI(const std::string &binFile, const cv::Mat &imRGB, cv::Mat &imDepth);
      void Transform3DPointFromLidarToImage02KITTI(const cv::Mat &x3Dl, cv::Mat &x3Dc, cv::Point2i &point2D, float &d);
       
      // backup funcitons
      void Transform3DPointFromLidarToCamera(const cv::Mat &x3Dl, const cv::Mat &Tcl, cv::Mat &x3Dc);
      void Transform3DPointInCameraTo2DImage(const cv::Mat &x3Dc, const cv::Mat &mK, cv::Point2i &point2D);
       
      template<typename T>
      void PrintCvMat(cv::Mat mat);
       
       
      int main()
      {
       
          // 1. read bin/pcd file and RGB file
          // 2. TransformBinFile2Points based on RGB image/ Tlc, TransformBinFile2ImDepth, GetPointsAndRGBCorrespondence
          // 2'. FOR LINGYUN: TransformPCDFile2Points
          std::string binFile = "/home/james/mytools/LidarPointReconstructionOneFrame/data/000005-lidar.bin";
          std::string rgbFile = "/home/james/mytools/LidarPointReconstructionOneFrame/data/000005-rgb.png";
       
          // test
          std::string pcdOutFile = "/home/james/mytools/LidarPointReconstructionOneFrame/data/000005-lidar.pcd";
       
          cv::Mat imRGB = cv::imread(rgbFile, cv::IMREAD_UNCHANGED);
          cv::Mat imDepth = cv::Mat::zeros(imRGB.rows, imRGB.cols, CV_32FC1);
       
          cv::Mat Tcl = (cv::Mat_<float>(4, 4)
                  << 1, 0, 0, 0,
                  0, 1, 0, 0,
                  0, 0, 1, 0,
                  0, 0, 0, 1);
       
          PointCloud::Ptr curentCloud;
       
          //curentCloud = TransformBinFile2Points(binFile, imRGB, Tcl, imDepth);
          curentCloud = TransformBinFile2PointsKITTI(binFile, imRGB,imDepth);
       
          // test: write to tiff file for imDepth
          cv::imwrite("/home/james/mytools/LidarPointReconstructionOneFrame/data/test_imDepth.tiff",
                      imDepth);
       
          // test: write to pcd file for curentCloud.
          pcl::io::savePCDFile(pcdOutFile, *curentCloud);
       
          std::cout << "Hello, World!" << std::endl;
          return 0;
      }
       
      // generate depth image and 3D points with RGB color in Camera Coordinate System
      // input: binFile of points from LIDAR, RGB image, Tcl(the transformation matrix from LIDAR to Camera)
      // output: imDepth and 3D Points with RGB color in the Camera Coordinate System
      PointCloud::Ptr TransformBinFile2PointsKITTI(const std::string &binFile, const cv::Mat &imRGB, cv::Mat &imDepth)
      {
          PointCloud::Ptr tmpCloud(new PointCloud);
       
          // load point cloud
          fstream input(binFile.c_str(), ios::in | ios::binary);
       
          if (!input.good())
          {
              cerr << "Could not read file: " << binFile << endl;
              exit(EXIT_FAILURE);
          }
          input.seekg(0, ios::beg);
       
          //read bin file one point by one point
          int i;
          cv::Point2i point2D(0, 0);
          float d = 0.0;
       
          for (i = 0; input.good() && !input.eof(); i++)
          {
              pcl::PointXYZI p; // the 3D point in the LIDAR coordinate system without color information
              input.read((char *) &p.x, 3 * sizeof(float));// read the value of x,y,z
              input.read((char *) &p.intensity, sizeof(float)); // For KITTI datasets
       
              if (p.x < 1E-6 && p.y < 1E-6 && p.z < 1E-6)
                  continue;
       
              // transform point3D in Lidar coordinate system to the camera
              cv::Mat x3Dl = (cv::Mat_<float>(3, 1) << p.x, p.y, p.z);
       
              // pniot 3D in camera02 in KITTI dataset
              cv::Mat x3Dc = cv::Mat::zeros(3, 1, CV_32F);
       
       
              Transform3DPointFromLidarToImage02KITTI(x3Dl, x3Dc, point2D, d);
              //PrintCvMat<float>(x3Dc);
       
              // d almost equal x3Dc.ptr<float>(2)[0]
              if (x3Dc.ptr<float>(2)[0] < 0 || d < 0)
                  continue;
       
              const int &x = point2D.x;
              const int &y = point2D.y;
              //std::cout << "point2D: " << point2D.x << ", " << point2D.y << std::endl;
       
              if (x > 0 && x < imRGB.cols &&
                  y > 0 && y < imRGB.rows)
              {
                  // generate depth image in current camera coordinate system
                  imDepth.ptr<float>(y)[x] = d; //TOTEST: or x3Dc.ptr<float>(2)[0];
       
                  // put point3D in camera to tmpCloud with RGB color information
                  PointT p3Dc;// the 3D point in the camera coordinate system with color information
       
                  p3Dc.x = x3Dc.ptr<float>(0)[0];
                  p3Dc.y = x3Dc.ptr<float>(1)[0];
                  p3Dc.z = x3Dc.ptr<float>(2)[0];
       
                  p3Dc.b = imRGB.ptr<uchar>(y)[x * 3];
                  p3Dc.g = imRGB.ptr<uchar>(y)[x * 3 + 1];
                  p3Dc.r = imRGB.ptr<uchar>(y)[x * 3 + 2];
       
                  tmpCloud->push_back(p3Dc);
              }
          }
       
          input.close();
       
          return tmpCloud;
      }
       
       
      // input: x3Dl
      // output: x3Dc and point2D
      void Transform3DPointFromLidarToImage02KITTI(const cv::Mat &x3Dl, cv::Mat &x3Dc, cv::Point2i &point2D, float &d)
      {
          // Vision meets Robotics: The KITTI Datase:
          // y = P_rect_02 * R_rect_00 * T_velo_to_cam00 * x
          cv::Mat P_rect_02 = (cv::Mat_<float>(3, 4)
                  << 7.188560000000e+02, 0.000000000000e+00, 6.071928000000e+02, 4.538225000000e+01,
                     0.000000000000e+00, 7.188560000000e+02, 1.852157000000e+02, -1.130887000000e-01,
                     0.000000000000e+00, 0.000000000000e+00, 1.000000000000e+00, 3.779761000000e-03 );
       
      //    cv::Mat P_rect_02_ = (cv::Mat_<float>(3, 4)
      //            << 7.188560000000e+02, 0.000000000000e+00, 6.071928000000e+02, 0,
      //            0.000000000000e+00, 7.188560000000e+02, 1.852157000000e+02, 0,
      //            0.000000000000e+00, 0.000000000000e+00, 1.000000000000e+00, 0 );
       
          cv::Mat R_rect_00 = (cv::Mat_<float>(4, 4)
                  << 9.999454e-01, 7.259129e-03, -7.519551e-03,   0,
                     -7.292213e-03, 9.999638e-01, -4.381729e-03,  0,
                     7.487471e-03, 4.436324e-03, 9.999621e-01,    0,
                     0,               0,          0,              1);
       
          cv::Mat T_velo_to_cam00 = (cv::Mat_<float>(4, 4)
                  << 7.967514e-03, -9.999679e-01, -8.462264e-04, -1.377769e-02,
                    -2.771053e-03, 8.241710e-04, -9.999958e-01, -5.542117e-02,
                     9.999644e-01, 7.969825e-03, -2.764397e-03, -2.918589e-01,
                     0,               0,          0,              1);
       
          // T_cam00_to_cam02 = (R_02, t_02)
          cv::Mat T_cam00_to_cam02 = (cv::Mat_<float>(4, 4)
                  <<  9.999788e-01, -5.008404e-03, -4.151018e-03,  5.954406e-02,
                      4.990516e-03, 9.999783e-01, -4.308488e-03,  -7.675338e-04,
                      4.172506e-03, 4.287682e-03, 9.999821e-01,   3.582565e-03,
                      0,               0,          0,              1);
       
       
          cv::Mat x3Dl_h = (cv::Mat_<float>(4, 1)
                  << x3Dl.ptr<float>(0)[0],
                     x3Dl.ptr<float>(1)[0],
                     x3Dl.ptr<float>(2)[0],
                     1                      );
       
          cv::Mat x3Dc_cam00 = T_velo_to_cam00 * x3Dl_h;
          cv::Mat rectified_x3Dc = P_rect_02 * R_rect_00 * x3Dc_cam00;
          //PrintCvMat<float>(rectified_x3Dc);
       
          // output the point 2D and depth in image02
          d = rectified_x3Dc.ptr<float>(2)[0];
          point2D.x = rectified_x3Dc.ptr<float>(0)[0]/d;
          point2D.y = rectified_x3Dc.ptr<float>(1)[0]/d;
          //std::cout << "point2D_rectified_x3Dc: " <<  point2D.x << ", " <<  point2D.y << ", d = "<< d << std::endl;
       
          cv::Mat theory_x3Dc = T_cam00_to_cam02 * x3Dc_cam00;
          //PrintCvMat<float>(theory_x3Dc);
       
          // output the point 3D in camera02
          x3Dc = (cv::Mat_<float>(3, 1)
                 <<   theory_x3Dc.ptr<float>(0)[0],
                      theory_x3Dc.ptr<float>(1)[0],
                      theory_x3Dc.ptr<float>(2)[0]);
       
          //TEST   // 跟 rectified_x3Dc.ptr<float>(2)[0]; 误差在3cm以内,后续可以对比效果做选择 d=? TODO
          //std::cout << "theory_x3Dc d = " <<  theory_x3Dc.ptr<float>(2)[0] << std::endl;
      }
       
       
      // generate depth image and 3D points with RGB color in Camera Coordinate System
      // input: binFile of points from LIDAR, RGB image, Tcl(the transformation matrix from LIDAR to Camera)
      // output: imDepth and 3D Points with RGB color in the Camera Coordinate System
      PointCloud::Ptr TransformBinFile2Points(const std::string &binFile, const cv::Mat &imRGB, const cv::Mat &Tcl, cv::Mat &imDepth)
      {
          //1. read bin file one point by one point => point3DInLidar
          //2. transform point3DInLidar to camera coordinate system  => point3DInCamera
          //3. transform point3DInCamera to Image coordinate system => point2DInImage
          //4. if point2DInImage in the plane of rgb image, put point2DInImage to imDepth
          //5. if point2DInImage in the plane of rgb image, put point3DInCamera to tmpCloud with RGB color information
       
          PointCloud::Ptr tmpCloud(new PointCloud);
       
          // load point cloud
          fstream input(binFile.c_str(), ios::in | ios::binary);
       
          if (!input.good())
          {
              cerr << "Could not read file: " << binFile << endl;
              exit(EXIT_FAILURE);
          }
          input.seekg(0, ios::beg);
       
          cv::Mat K = (cv::Mat_<float>(3, 3)
                  << 1, 0, 0,
                  0, 1, 0,
                  0, 0, 1);
       
          //read bin file one point by one point
          int i;
          cv::Point2i point2D(0, 0);
          for (i = 0; input.good() && !input.eof(); i++)
          {
              pcl::PointXYZI p; // the 3D point in the LIDAR coordinate system without color information
              input.read((char *) &p.x, 3 * sizeof(float));// read the value of x,y,z
              input.read((char *) &p.intensity, sizeof(float)); // For KITTI datasets
       
              if (p.x < 1E-6 && p.y < 1E-6 && p.z < 1E-6)
                  continue;
       
              // transform point3D in Lidar coordinate system to the camera
              cv::Mat x3Dl = (cv::Mat_<float>(3, 1) << p.x, p.y, p.z);
              cv::Mat x3Dc = cv::Mat::zeros(3, 1, CV_32F);
       
              PrintCvMat<float>(x3Dl);
              Transform3DPointFromLidarToCamera(x3Dl, Tcl, x3Dc);
              PrintCvMat<float>(x3Dc);
       
              if (x3Dc.ptr<float>(2)[0] < 0)
                  continue;
       
              // transform point3D in camera to point2D in pixel coordinate system
              point2D.x = 0;
              point2D.y = 0;
              Transform3DPointInCameraTo2DImage(x3Dc, K, point2D);
       
              const int &x = point2D.x;
              const int &y = point2D.y;
              std::cout << "point2D: " << point2D.x << ", " << point2D.y << std::endl;
              if (x > 0 && x < imRGB.cols &&
                  y > 0 && y < imRGB.rows)
              {
                  // generate depth image in current camera coordinate system
                  imDepth.ptr<float>(y)[x] = x3Dc.ptr<float>(2)[0];
       
                  // put point3D in camera to tmpCloud with RGB color information
                  PointT p3Dc;// the 3D point in the camera coordinate system with color information
                  p3Dc.z = x3Dc.ptr<float>(2)[0];
                  p3Dc.x = x3Dc.ptr<float>(0)[0];
                  p3Dc.y = x3Dc.ptr<float>(1)[0];
                  p3Dc.b = imRGB.ptr<uchar>(y)[x * 3];
                  p3Dc.g = imRGB.ptr<uchar>(y)[x * 3 + 1];
                  p3Dc.r = imRGB.ptr<uchar>(y)[x * 3 + 2];
                  tmpCloud->push_back(p3Dc);
              }
          }
       
          // test
          cv::imwrite("/home/james/mytools/LidarPointReconstructionOneFrame/data/test_imDepth.tiff",
                      imDepth);
       
          input.close();
          return tmpCloud;
      }
       
      // transform the 3D points form LIDAR coordinate system to camera coordinate system
      // input:  x3Dl and Tcl
      // output: x3Dc
      void Transform3DPointFromLidarToCamera(const cv::Mat &x3Dl, const cv::Mat &Tcl, cv::Mat &x3Dc)
      {
          cv::Mat Rcl = Tcl.rowRange(0, 3).colRange(0, 3);
          cv::Mat tcl = Tcl.rowRange(0, 3).col(3);
          x3Dc = Rcl * x3Dl + tcl;
       
      }
       
      // transform the 3D points in camera coordinate system to pixel coordinate system
      // input: x3Dc and K
      // output: point2D
      void Transform3DPointInCameraTo2DImage(const cv::Mat &x3Dc, const cv::Mat &mK, cv::Point2i &point2D)
      {
          const float &fx = mK.at<float>(0, 0);
          const float &fy = mK.at<float>(1, 1);
          const float &cx = mK.at<float>(0, 2);
          const float &cy = mK.at<float>(1, 2);
       
          point2D.x = (int) (fx * x3Dc.at<float>(0, 0) / x3Dc.at<float>(2, 0) + cx); // fx * X / Z + cx
          point2D.y = (int) (fy * x3Dc.at<float>(1, 0) / x3Dc.at<float>(2, 0) + cy); // fx * Y / Z + cy
       
       
      }
       
       
      template<typename T>
      void PrintCvMat(cv::Mat mat)
      {
          T value;
          cout << "\n-------------------------------" << endl;
          for (int j = 0; j < mat.rows; j++)
          {
              for (int i = 0; i < mat.cols; i++)
              {
                  value = mat.ptr<T>(j)[i];
                  cout << value << "\t\t";
              }
              cout << endl;
          }
          cout << "\n-------------------------------" << endl;
      }

CMakeLists.txt


    1. cmake_minimum_required(VERSION 3.17)
      project(LidarPointReconstructionOneFrame)
      SET(CMAKE_BUILD_TYPE "Debug")
       
      set(CMAKE_CXX_STANDARD 11)
       
      find_package(OpenCV 3)
      if(NOT OpenCV_FOUND)
          find_package(OpenCV 2.4.3 QUIET)
          if(NOT OpenCV_FOUND)
              message(FATAL_ERROR "OpenCV > 2.4.3 not found.")
          endif()
      endif()
       
      find_package(Eigen3 3.1.0 REQUIRED)
       
      find_package( PCL REQUIRED COMPONENT common io visualization filters)
      set(PCL_INCLUDE_DIRS /usr/include/pcl-1.8)
      link_directories(${PCL_LIBRARY_DIRS})
       
      add_definitions( ${PCL_DEFINITIONS} )
       
      include_directories(
              ${EIGEN3_INCLUDE_DIR}
              ${PCL_INCLUDE_DIRS}
      )
       
      add_executable(LidarPointReconstructionOneFrame main.cpp)
       
      target_link_libraries(${PROJECT_NAME}
              ${OpenCV_LIBS}
              ${EIGEN3_LIBS}
              ${PCL_LIBRARIES}
              )

效果:

彩色点云图:

 与RGB图像对应的深度图:

 

参考:


转载自:https://visionary.blog.csdn.net/article/details/113180123