LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping
Name
2007.00258.pdf
Description
Submitted version
Size
4.17 MB
Format
Adobe PDF
Checksum (MD5)
01ef7d96fe127c56db05d8bbdc174c87
Author(s)
Shan, Tixiao
Englot, Brendan
Meyers, Drew
Wang, Wei
Ratti, Carlo
Rus, Daniela
Date Issued
2020
Journal
IEEE International Conference on Intelligent Robots and Systems
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Shan, Tixiao, Englot, Brendan, Meyers, Drew, Wang, Wei, Ratti, Carlo et al. 2020. "LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping." IEEE International Conference on Intelligent Robots and Systems.
Version
Original manuscript
Abstract
© 2020 IEEE. We propose a framework for tightly-coupled lidar inertial odometry via smoothing and mapping, LIO-SAM, that achieves highly accurate, real-time mobile robot trajectory estimation and map-building. LIO-SAM formulates lidar-inertial odometry atop a factor graph, allowing a multitude of relative and absolute measurements, including loop closures, to be incorporated from different sources as factors into the system. The estimated motion from inertial measurement unit (IMU) pre-integration de-skews point clouds and produces an initial guess for lidar odometry optimization. The obtained lidar odometry solution is used to estimate the bias of the IMU. To ensure high performance in real-time, we marginalize old lidar scans for pose optimization, rather than matching lidar scans to a global map. Scan-matching at a local scale instead of a global scale significantly improves the real-time performance of the system, as does the selective introduction of keyframes, and an efficient sliding window approach that registers a new keyframe to a fixed-size set of prior "sub-keyframes."The proposed method is extensively evaluated on datasets gathered from three platforms over various scales and environments.
MIT Department
Massachusetts Institute of Technology. Department of Urban Studies and Planning
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/IROS45743.2020.9341176
