LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping
© 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 re...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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Institute of Electrical and Electronics Engineers (IEEE)
2022
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| Online Access: | https://hdl.handle.net/1721.1/144041 |
| _version_ | 1826208882747244544 |
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| author | Shan, Tixiao Englot, Brendan Meyers, Drew Wang, Wei Ratti, Carlo Rus, Daniela |
| author2 | Massachusetts Institute of Technology. Department of Urban Studies and Planning |
| author_facet | Massachusetts Institute of Technology. Department of Urban Studies and Planning Shan, Tixiao Englot, Brendan Meyers, Drew Wang, Wei Ratti, Carlo Rus, Daniela |
| author_sort | Shan, Tixiao |
| collection | MIT |
| description | © 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. |
| first_indexed | 2024-09-23T14:14:08Z |
| format | Article |
| id | mit-1721.1/144041 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T14:14:08Z |
| publishDate | 2022 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1440412023-02-10T20:32:31Z LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping Shan, Tixiao Englot, Brendan Meyers, Drew Wang, Wei Ratti, Carlo Rus, Daniela Massachusetts Institute of Technology. Department of Urban Studies and Planning Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory © 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. 2022-07-26T12:53:49Z 2022-07-26T12:53:49Z 2020 2022-07-26T12:47:09Z Article http://purl.org/eprint/type/ConferencePaper https://hdl.handle.net/1721.1/144041 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. en 10.1109/IROS45743.2020.9341176 IEEE International Conference on Intelligent Robots and Systems Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) arXiv |
| spellingShingle | Shan, Tixiao Englot, Brendan Meyers, Drew Wang, Wei Ratti, Carlo Rus, Daniela LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping |
| title | LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping |
| title_full | LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping |
| title_fullStr | LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping |
| title_full_unstemmed | LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping |
| title_short | LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping |
| title_sort | lio sam tightly coupled lidar inertial odometry via smoothing and mapping |
| url | https://hdl.handle.net/1721.1/144041 |
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