Publication: High-accuracy patternless calibration of multiple 3D LiDARs for autonomous vehicles
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2023-06-01
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IEEE
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Abstract
This article proposes a new method for estimating the extrinsic calibration parameters between any pair of multibeam LiDAR sensors on a vehicle. Unlike many state-of-the-art works, this method does not use any calibration pattern or reflective marks placed in the environment to perform the calibration; in addition, the sensors do not need to have overlapping fields of view. An iterative closest point (ICP)-based process is used to determine the values of the calibration parameters, resulting in better convergence and improved accuracy. Furthermore, a setup based on the car learning to act (CARLA) simulator is introduced to evaluate the approach, enabling quantitative assessment with ground-truth data. The results show an accuracy comparable with other approaches that require more complex procedures and have a more restricted range of applicable setups. This work also provides qualitative results on a real setup, where the alignment between the different point clouds can be visually checked. The open-source code is available at https://github.com/midemig/pcd_calib .
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Autonomous driving, Extrinsic calibration, Iterative closest point, LiDAR, Sensor fusion
Bibliographic citation
De Miguel, M. Á., Guindel, C., Al-Kaff, A., & García, F. (2023). High-Accuracy Patternless Calibration of Multiple 3D LiDARs for Autonomous Vehicles. IEEE Sensors Journal, 23(11), 12200-12208.