LB-L2L-Calib 2.0: A Novel Online Extrinsic Calibration Method for Multiple Long Baseline 3D LiDARs Using Objects

In V2X (Vehicle-to-Everything), one important work is to extrinsically calibrate multiple 3D LiDARs, which are mounted with a long baseline and large viewpoint-difference at the road-side. Current solutions either require a specific target being set up (e.g., a sphere), or require specific features existing in the environment (e.g., mutually orthogonal planes). However, it is time-consuming, sometimes even inconvenient, to set up specific targets, e.g., at busy intersections and highways. Furthermore, specific features do not always exist in the traffic scenario. Thus, the current solutions are not feasible. To address this problem, a novel extrinsic calibration method is proposed in this paper, namely LB-L2L-Calib 2.0. It is the 2.0 version of our previous work. The novelties are: 1) We propose to use the easily accessible objects on the road as features for calibration (i.e., the vehicles). Thus, it is not necessary to set up any specific targets and we do not need to worry whether specific features exist or not. The key point is we observed that the 3D bounding box centers of the vehicles are viewpoint-invariant from different viewpoints, which makes them ideal features for long baseline and large viewpoint-difference calibration. 2) To establish correct correspondence between the bounding box centers detected from different LiDARs, we propose an exhaustive searching strategy. It can robustly output correct correspondence. Extensive experiments are performed in three scenarios (simulation: intersection, real: carpark and highway), with two types of LiDAR (Velodyne and Livox), demonstrating that LB-L2L-Calib 2.0 is robust, effective, and accurate.