SPLiCE: Single-Point LiDAR and Camera Calibration & Estimation Leveraging Manhattan World

Abstract

We present a novel calibration method between single-point LiDAR and camera sensors utilizing an easy-to-build customized calibration board satisfying the Manhattan world (MW). Previous methods for LiDAR-camera (LC) calibration focus on line and plane correspondences. However, they require dense 3D point clouds from heavy and expensive LiDAR to simplify alignments; otherwise, these approaches fail for extremely sparse LiDAR. Compact, lightweight, and sparse LiDAR and camera sensors are inevitable for micro drones like Crazyflie with a maximum payload of 15 g, but there are no explicit calibration methods for them. To address these issues, we propose a new extrinsic calibration method with a new calibration board, which rotates like a door to capture geometric features and align them with images. Once we find an initial estimate, we refine the relative rotation by minimizing the angle difference between the grid orientation of the checkerboard and the MW axes. We demonstrate the effectiveness of the proposed method through various LC configurations, achieving its capability and high accuracy compared to other state-of-the-art approaches. We release our calibration toolkit, source codes, and how to make the calibration boards for the robotics community: https://SPLiCE-Calib.github.io/.