📝 Automatic Targetless Lidar Camera Calibration

Auto-calibration of lidar and camera based on maximization of intensity mutual information. This is the reimplementation of the paper: Automatic Targetless Extrinsic Calibration of a 3D Lidar and Camera by Maximizing Mutual Information

🎉 TODO

reimplementation of Automatic Targetless Extrinsic Calibration of a 3D Lidar and Camera by Maximizing Mutual Information
test on real lidar, camera data

sudo apt-get install \
    libopencv-dev \
    libpcl-dev \
    rapidjson-dev \

make default -j`nproc`

# build examples
make apps -j`nproc`

Download livox lidar data together with images from here(If you are interested, you can search for more about low-cost livox lidars).
Extract the sample data, and create two files images.txt and point_clouds.txt that store absolute paths to image, pcd data file respectively. One line for one file.
Create camera info with camera instrinsic matrix, a sample is provided here
Create initial guess of transformation info from lidar to camera, in the form of translation(tx, ty, tz), rotation(roll, pitch, yaw); a sample is provided here
Fill absolute paths to the above files in calibration_handler_param.json
Run (after make apps)

    ./build/examples/sensors_calib_app ./data/samples/calibration_handler_param.json

After the optimization finishes, the final transformation info will be printed out.

Also the projected (image to) pointclouds; (pointcloud to) images will be saved. Check cloud*.pcd and img*.png files.

projected cloud:

projected image:

Automatic Targetless Extrinsic Calibration of a 3D Lidar and Camera by Maximizing Mutual Information, AAAI 2012
Automatic Calibration of Lidar and Camera Images using Normalized Mutual Information, ICRA 2013
Accurate Extrinsic Calibration between Monocular Camera and Sparse 3D Lidar Points without Markers, IV 2017