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PBACalib

Description

• This is the original work on livox-camera extrinsic calibration. The corresponding paper is " Feiyi. Chen, Liang. Li, Shuyang. Zhang, Jin. Wu and Lujia. Wang, "PBACalib: Targetless Extrinsic Calibration for High-Resolution LiDAR-Camera System Based on Plane-Constrained Bundle Adjustment," in IEEE Robotics and Automation Letters, 2022.". You can visit our website to find more details and the supplementary
• This work is implemented by Matlab. <img src="matlab/figures/overview.png" width = "60%" alt="Overview" align=center />

Project structure

├─matlab    The code to perform calibration
│ ├─colmap   Related tools to process the data exported from colamp
│ ├─LM_solver
│ │ ├─jocbian
│ │ └─obj
│ └─utils
├─ros_ws    The related cpp code to collect the data for the calibration
└─shell     Shell scripts to perform SFM, which will call the exec files in colmap

Data Preparation

• Calibration Scene
  • First find a calibration scene, which is a plane with arbitrary texture. The calibration accuracy performs better when 1) texture is rich 2) the plane is strictly flat 3) the background is clean.
  • The example scenes are shown as follows
    
• Collection tools
  • We supply tools to collect images and point could. For our sensors operate in ROS framework, we write c++ tools to subscribe ROS topic and save data.
  • The tool is in folder ros_ws, which is a ros workspace. Run following command to build and execuate the tool

      catkin_make
      rosrun livox_cam_tools liv_map_cam_recorder
    

    It will print help notes to tell you what parameters you need to specify, as follows
• Undistorted images Run matlab file undist_imgs.m to undistort all images. Please change the intrinsic, distortion matrix and data file path.
• Default data structure

  data/img/1.png (raw images)
  data/img/2.png (raw images) 
  ...
  ----------------------------
  data/img_un/1.png (undistorted images)
  data/img_un/2.png (undistorted images) 
  ...
  ----------------------------
  data/pcd/1.pcd (raw pcds)
  data/pcd/2.pcd (raw pcds)
  
  

Estimate camera poses using structure from motion

We use colmap to conduct SfM and export model files as txt into folder "models". Then the default data structure is shown as follows

 data/img/1.png (raw images)
 data/img/2.png (raw images) 
 ...
--------------------------
 data/img_un/1.png (undistorted images)
 data/img_un/2.png (undistorted images) 
 ...
--------------------------
 data/pcd/1.pcd (raw pcds)
 data/pcd/2.pcd (raw pcds)
 ...
--------------------------
 models/cameras.txt
 models/images.txt
 models/points3D.txt
 models/project.ini

Please read readme files in colmap to learn how to conduct SfM

Calibration

Run "main_cali_real.m" file in matlab folder to calibrate the extrinsics between camera and dense LiDAR. Please modify the parameters in "main_cali_real.m", which contains

K = [897.4566,0,635.4040;
  0,896.7992,375.3149;
    0,0,1];
D = [-0.4398 0.2329 -0.0011 2.0984e-04 -0.0730];

TInit = [0.0324   -0.9994    0.0130   -0.0152
    0.0215   -0.0123   -0.9997    0.0695
    0.9992    0.0327    0.0211   -0.0132
    0         0         0    1.0000];
data_path = "/home/cfy/Documents/livoxBACali/data/real/scene2/";
pcd_folder = data_path+"pcd";
img_folder = data_path+"img_un";

The extrinsics and projection result will show automatically when finished.

Data

• simulation environment: based on gazebo, we published on this repo

• the collected real and simulation data is placed on the google drive

If you use this project for your research, please cite:

@ARTICLE{chen2022pbacalib,
  author={Chen, Feiyi and Li, Liang and Zhang, Shuyang and Wu, Jin and Wang, Lujia},
  journal={IEEE Robotics and Automation Letters}, 
  title={PBACalib: Targetless Extrinsic Calibration for High-Resolution LiDAR-Camera System Based on Plane-Constrained Bundle Adjustment}, 
  year={2023},
  volume={8},
  number={1},
  pages={304-311},
  doi={10.1109/LRA.2022.3226026}}

TODO

• Please feel free to report issue