Automatic Calibration of Multiple 3D LiDARs in Urban Environments
This addresses the calibration bottleneck for multi-LiDAR systems in urban autonomous driving, enabling improved localization and perception.
The paper tackles the problem of calibrating multiple 3D LiDARs on autonomous vehicles without manual intervention, achieving rotation errors under 0.04 rad and translation errors under 0.1 m.
Multiple LiDARs have progressively emerged on autonomous vehicles for rendering a wide field of view and dense measurements. However, the lack of precise calibration negatively affects their potential applications in localization and perception systems. In this paper, we propose a novel system that enables automatic multi-LiDAR calibration without any calibration target, prior environmental information, and initial values of the extrinsic parameters. Our approach starts with a hand-eye calibration for automatic initialization by aligning the estimated motions of each sensor. The resulting parameters are then refined with an appearance-based method by minimizing a cost function constructed from point-plane correspondences. Experimental results on simulated and real-world data sets demonstrate the reliability and accuracy of our calibration approach. The proposed approach can calibrate a multi-LiDAR system with the rotation and translation errors less than 0.04 [rad] and 0.1 [m] respectively for a mobile platform.