Paper Info
Title
Robust Odometry and Mapping for Multi-LiDAR Systems With Online Extrinsic Calibration
Abstract
Combining multiple LiDARs enables a robot to maximize its perceptual awareness of environments and obtain sufficient measurements, which is promising for simultaneous localization and mapping (SLAM). This article proposes a system to achieve robust and simultaneous extrinsic calibration, odometry, and mapping for multiple LiDARs. Our approach starts with measurement preprocessing to extract edge and planar features from raw measurements. After a motion and extrinsic initialization procedure, a sliding window-based multi-LiDAR odometry runs onboard to estimate poses with an online calibration refinement and convergence identification. We further develop a mapping algorithm to construct a global map and optimize poses with sufficient features together with a method to capture and reduce data uncertainty. We validate our approach’s performance with extensive experiments on 10 sequences (4.60-km total length) for the calibration and SLAM and compare it against the state of the art. We demonstrate that the proposed work is a complete, robust, and extensible system for various multi-LiDAR setups. The source code, datasets, and demonstrations are available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://ram-lab.com/file/site/m-loam</uri> .
Year
DOI
Venue
2022
10.1109/TRO.2021.3078287
IEEE Transactions on Robotics
Keywords
DocType
Volume
Autonomous driving,calibration and identification,sensor fusion,simultaneous localization and mapping (SLAM)
Journal
38
Issue
ISSN
Citations 
1
1552-3098
1
PageRank 
References 
Authors
0.35
47
4
Name
Order
Citations
PageRank
Jianhao Jiao1196.68
Haoyang Ye2176.84
Yilong Zhu366.16
Ming Liu477594.83