TY - GEN
T1 - An Adaptive Overlap-based ICP Algorithm for Multi-LiDAR Calibration in Low-overlap Situations
AU - Li, Hanlin
AU - Yuan, Huajun
AU - Zhao, Haocheng
AU - Zou, Bin
AU - Yu, Limin
AU - Wang, Xinheng
N1 - Funding Information:
ACKNOWLEDGMENT The authors would like to thank Suzhou Inteleizhen Intelligent Technology Co. Ltd and Xi’an Jiaotong-Liverpool University for their financial support by Key Program Special Fund in XJTLU under project KSF-E-64, XJTLU Research Development Fund under projects RDF-19-01-14 and RDF-20-01-15, and the National Natural Science Foundation of China (NSFC) under grant 52175030. Thanks also to Tongpo Zhang for his guidance in academic writing.
Publisher Copyright:
© 2022 IEEE.
PY - 2022/12/10
Y1 - 2022/12/10
N2 - LiDAR has been widely used as a core sensor in autonomous driving and mobile robotics. Due to the high price of spinning LiDARs, the industry always uses a combination of multiple calibrated solid-state LiDARs. However, there is usually only a small overlap FoV between LiDARs on account of their mounting positions. In such a low-overlap situation, conventional calibration methods always bring catastrophic results. To solve this problem, this paper proposed an adaptive overlap-based ICP algorithm, which can calibrate both 2D and 3D LiDARs. The algorithm adaptively extracts the overlapping point clouds by identifying the correct matching results and constructs an optimized ICP alignment. By controlling the radius range of the point cloud search, our method allows for quick extrinsic calibration of two LiDARs with low-overlap FoV. Experiments show that our algorithm achieves a more robust and accurate low-overlap registration than other methods, and also has speed advantages in general situations.
AB - LiDAR has been widely used as a core sensor in autonomous driving and mobile robotics. Due to the high price of spinning LiDARs, the industry always uses a combination of multiple calibrated solid-state LiDARs. However, there is usually only a small overlap FoV between LiDARs on account of their mounting positions. In such a low-overlap situation, conventional calibration methods always bring catastrophic results. To solve this problem, this paper proposed an adaptive overlap-based ICP algorithm, which can calibrate both 2D and 3D LiDARs. The algorithm adaptively extracts the overlapping point clouds by identifying the correct matching results and constructs an optimized ICP alignment. By controlling the radius range of the point cloud search, our method allows for quick extrinsic calibration of two LiDARs with low-overlap FoV. Experiments show that our algorithm achieves a more robust and accurate low-overlap registration than other methods, and also has speed advantages in general situations.
KW - 2D LiDAR
KW - 3D LiDAR
KW - ICP
KW - multi-LiDAR calibration
KW - overlap-based
KW - point cloud registration
UR - http://www.scopus.com/inward/record.url?scp=85146427260&partnerID=8YFLogxK
U2 - 10.1109/HDIS56859.2022.9991525
DO - 10.1109/HDIS56859.2022.9991525
M3 - Conference Proceeding
AN - SCOPUS:85146427260
T3 - 2022 International Conference on High Performance Big Data and Intelligent Systems, HDIS 2022
SP - 54
EP - 58
BT - 2022 International Conference on High Performance Big Data and Intelligent Systems, HDIS 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th International Conference on High Performance Big Data and Intelligent Systems, HDIS 2022
Y2 - 10 December 2022 through 11 December 2022
ER -