Unified calibration for multi-camera multi-lidar systems using a single checkerboard

Abstract

In this paper, we propose a unified calibration method for multi-camera multi-LiDAR systems. Only using a single planar checkerboard, the captured checkerboard frames by each sensor are classified as either global frames if they are observed by at least two sensors, or a local frame if observed by a single camera. Both global and local frames of each camera are used to estimate its intrinsic parameters, whereas the global frames between sensors are for computing their relative poses. In contrast to the previous methods that simply combine the pairwise poses (e.g., camera-to-camera or camera-to-LiDAR) that are separately estimated, we further optimize the sensor poses in the system globally using all observations as the constraints in the optimization problem. We find that the point-to-plane distances are effective as camera-to-LiDAR constraints where the points are 3D positions of the checkerboard corners and the planes are estimated from the LiDAR point-cloud. Also, abundant corner observations in the local frames enable the joint optimization of intrinsic and extrinsic parameters in a unified framework. The proposed calibration method utilizes entire observations in a unified global optimization framework, and it significantly reduces the error caused by a simple composition of the relative sensor poses. We extensively evaluate the proposed algorithm qualitatively and quantitatively using real and synthetic datasets. We plan to make the implementation open to the public with the paper publication.