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LiDAR Iris for Loop-Closure Detection
Name
1912.03825.pdf
Description
Submitted version
Size
5.27 MB
Format
Adobe PDF
Checksum (MD5)
1be244b8d6ac96948c59854228637314
Author(s)
Wang, Ying
Sun, Zezhou
Xu, Cheng-Zhong
Sarma, Sanjay E
Yang, Jian
Kong, Hui
Date Issued
2020
Journal
IEEE International Conference on Intelligent Robots and Systems
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Wang, Ying, Sun, Zezhou, Xu, Cheng-Zhong, Sarma, Sanjay E, Yang, Jian et al. 2020. "LiDAR Iris for Loop-Closure Detection." IEEE International Conference on Intelligent Robots and Systems.
Version
Original manuscript
Abstract
© 2020 IEEE. In this paper, a global descriptor for a LiDAR point cloud, called LiDAR Iris, is proposed for fast and accurate loop-closure detection. A binary signature image can be obtained for each point cloud after several LoG-Gabor filtering and thresholding operations on the LiDAR-Iris image representation. Given two point clouds, their similarities can be calculated as the Hamming distance of two corresponding binary signature images extracted from the two point clouds, respectively. Our LiDAR-Iris method can achieve a pose-invariant loop-closure detection at a descriptor level with the Fourier transform of the LiDAR-Iris representation if assuming a 3D (x, y, yaw) pose space, although our method can generally be applied to a 6D pose space by re-aligning point clouds with an additional IMU sensor. Experimental results on five road-scene sequences demonstrate its excellent performance in loop-closure detection.
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
10.1109/IROS45743.2020.9341010
