xiaoxueshengyao / lidar_to_camera

Projection of Lidar 3d point cloud to 2d image plane

Lidar 3D Cloud Projection

This Project present how to associate regions in a camera image with Lidar points in 3D space.

For more details, please check my blog.

Dependencies for Running Locally

• cmake >= 2.8
  • All OSes: click here for installation instructions
• make >= 4.1 (Linux, Mac), 3.81 (Windows)
  • Linux: make is installed by default on most Linux distros
  • Mac: install Xcode command line tools to get make
  • Windows: Click here for installation instructions
• Git LFS
  • Weight files are handled using LFS
• OpenCV >= 4.1
  • This must be compiled from source using the -D OPENCV_ENABLE_NONFREE=ON cmake flag for testing the SIFT and SURF detectors.
  • The OpenCV 4.1.0 source code can be found here
• gcc/g++ >= 5.4
  • Linux: gcc / g++ is installed by default on most Linux distros
  • Mac: same deal as make - install Xcode command line tools
  • Windows: recommend using MinGW

Basic Build Instructions

1. Clone this repo.

2. Make a build directory in the top level project directory: mkdir build && cd build

3. Compile: cmake .. && make

4. Run it: ./project_lidar_to_camera.

Steps

1. Filtering Lidar Points

   The code below shows how a filter can be applied to remove Lidar points that do not satisfy a set of constraints, i.e. they are …

   1. … positioned behind the Lidar sensor and thus have a negative x coordinate.
   2. … too far away in x-direction and thus exceeding an upper distance limit.
   3. … too far off to the sides in y-direction and thus not relevant for collision detection
   4. … too close to the road surface in negative z-direction.
   5. … showing a reflectivity close to zero, which might indicate low reliability.

   for(auto it=lidarPoints.begin(); it!=lidarPoints.end(); ++it) {
       float maxX = 25.0, maxY = 6.0, minZ = -1.4; 
       if(it->x > maxX || it->x < 0.0 || abs(it->y) > maxY || it->z < minZ || it->r<0.01 )
       {
       	continue; // skip to next point
   	}
   }

2. Convert current Lidar point into homogeneous coordinates and store it in the 4D variable X.

    X.at<double>(0, 0) = it->x;
    X.at<double>(1, 0) = it->y;
    X.at<double>(2, 0) = it->z;
    X.at<double>(3, 0) = 1;
   

3. Then, apply the projection equation to map X onto the image plane of the camera and Store the result in Y.

   Y = P_rect_00 * R_rect_00 * RT * X;
   

4. Once this is done, transform Y back into Euclidean coordinates and store the result in the variable pt.

   cv::Point pt;
   pt.x = Y.at<double>(0, 0) / Y.at<double>(0, 2);
   pt.y = Y.at<double>(1, 0) / Y.at<double>(0, 2);