Extrinsic Calibration of Camera and LiDAR Systems With Three-Dimensional Towered Checkerboards

Abstract

With the increasing utilization of cameras and three-dimensional Light Detection and Ranging (LiDAR) systems in perception tasks, the fusion of these two sensor modalities has emerged as a prominent research focus in the fields of robotics and unmanned systems. While various extrinsic calibration methods have been developed, they often suffer from limited accuracy when using low-resolution LiDAR sensors and require the placement of calibration targets at multiple locations. This paper introduces a novel calibration target known as the Three-Dimensional Towered Checkerboard (3TC), along with a precise and straightforward extrinsic calibration approach for camera-LiDAR systems. The 3TC consists of stacked cubes adorned with planar or 2D checkerboards, which provide the known positions of checkerboard corner points in three-dimensional space. Leveraging the Iterative Closest Point (ICP) algorithm, the proposed method calculates the spatial relationship between LiDAR point cloud data and the 3TC model to infer the positions of checkerboard corner points in the LiDAR coordinate system. Subsequently, the Perspective-n-Point (PnP) algorithm is employed to establish the correlation between corner positions in the LiDAR coordinate system and the camera image, given the intrinsic parameters of the camera. By ensuring an adequate number of cubes and 2D checkerboards on a specific 3TC, along with accurately estimated corner point positions in LiDAR, a single frame of data from both the camera and LiDAR facilitates their extrinsic calibration. Experimental validations conducted across diverse camera and LiDAR systems, achieving minimal error close to the theoretical limit of the devices, attest to the robustness and precision of the 3TC and the proposed calibration methodology.