How are cameras and lidar calibrated? An overview of all mainstream calibration tools in the industry

The calibration of cameras and lidar is a crucial basic work in many tasks. The accuracy of calibration directly affects the upper limit of the effect of subsequent solution fusion. As many autonomous driving and robotics companies invest a lot of manpower and material resources to continuously improve the accuracy of calibration, today we will introduce you to some common Camera-Lidar calibration toolboxes, and we recommend that you collect them!

1.Libcbdetect

Multiple checkerboard detection in one shot: https://www.cvlibs.net/software/libcbdetect/

Use MATLAB The algorithm was written to automatically extract corner points and combine them with sub-pixel accuracy into a rectangular checkerboard-like pattern. This algorithm has the ability to process different types of images (such as pinhole cameras, fisheye cameras, panoramic cameras).

2.Autoware Calibration Package

Autoware framework lidar-camera calibration tool kit.

Link: https://github.com/autowarefoundation/autoware_ai_utilities/tree/master/autoware_camera_lidar_calibrator

##3. Based on 3D-3D matching Target calibration

LiDAR camera calibration based on 3D-3D point correspondences, ROS package, from the paper "LiDAR-Camera Calibration using 3D-3D Point correspondences"!

Link: https://github.com/ankitdhall/lidar_camera_calibration

4. Shanghai AI Lab OpenCalib

Produced by Shanghai Artificial Intelligence Laboratory, OpenCalib provides a sensor calibration toolbox. The toolbox can be used to calibrate sensors such as IMU, lidar, camera and radar.

Link: https://github.com/PJLab-ADG/SensorsCalibration

##5. Apollo calibration tool

Apollo calibration toolbox, link: https://github.com/ApolloAuto/apollo/tree/master/modules/calibration

6.Livox -camera calibration tool

This solution provides a method to manually calibrate the external parameters between Livox radar and camera, and has been verified on Mid-40, Horizon and Tele-15. It includes codes related to calculating camera internal parameters, obtaining calibration data, optimizing calculation of external parameters and radar camera fusion applications. In this solution, the corner points of the calibration plate are used as the calibration target. Due to the non-repetitive scanning characteristics of the Livox radar, the density of the point cloud is relatively large, making it easier to find the accurate position of the corner points in the radar point cloud. Calibration and fusion of camera radar can also give good results.

Link: https://github.com/Livox-SDK/livox_camera_lidar_calibration

Chinese documentation: https://github.com/Livox-SDK/livox_camera_lidar_calibration/blob/master/doc_resources/ README_cn.md

7.CalibrationTools

CalibrationTools provides calibration tools for lidar-lidar, lidar camera and other sensor pairs. In addition to this, it also provides:

1) Positioning - Bias estimation tool estimates the parameters of sensors used for dead reckoning (IMU and odometry) for better positioning performance!

2) Visualization and analysis tool for Autoware control output;

3) Calibration tool for fixing vehicle command delays;

Link: https://github.com /tier4/CalibrationTools

##8.Matlab

Matlab’s own toolbox supports lidar and camera calibration, link ：https://ww2.mathworks.cn/help/lidar/ug/lidar-and-camera-calibration.html

9.ROS Calibration Tool

ROS Camera LIDAR Calibration Package, link: https://github.com/heethesh/lidar_camera_calibration

10.Direct visual lidar calibration

This package provides a toolbox for lidar camera calibration:

Universal: It can handle a variety of lidar and camera projection models, including rotation and Non-repetitive scanning lidar, as well as pinhole, fisheye and omnidirectional projection cameras. No target: It does not require calibration targets, but uses environment structures and textures for calibration. Single shot: Calibration only requires at least a pair of lidar point clouds and camera images. Optionally, multiple lidar camera data pairs can be used to improve accuracy. Automatic: The calibration process is automatic and requires no initial guessing. Accurate and robust: It uses a pixel-level direct lidar camera registration algorithm, which is more robust and accurate compared to edge-based indirect lidar camera registration.

Link: https://github.com/koide3/direct_visual_lidar_calibration

11.2D lidar-camera toolbox

This is a ROS-based Single-line laser and camera external parameters automatic calibration code. The calibration principle is shown in the figure below. The camera estimates the plane equation of the calibration plate plane in the camera coordinate system through the QR code. Since the laser point cloud falls on the plane, the point cloud is converted to the external parameters of the camera coordinate system through the laser coordinate system. In the camera coordinate system, the distance from the constructed point to the plane is used as the error, which is solved using nonlinear least squares.

Link: https://github.com/MegviiRobot/CamLaserCalibraTool

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