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- Publisher Website: 10.1109/LRA.2021.3064227
- Scopus: eid_2-s2.0-85102545612
- WOS: WOS:000633394300021
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Article: Fast-lio: A fast, robust lidar-inertial odometry package by tightly-coupled iterated Kalman filter
Title | Fast-lio: A fast, robust lidar-inertial odometry package by tightly-coupled iterated Kalman filter |
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Authors | |
Keywords | Aerial systems localization perception and autonomy sensor fusion |
Issue Date | 2021 |
Publisher | Institute of Electrical and Electronics Engineers. The Journal's web site is located at https://www.ieee.org/membership-catalog/productdetail/showProductDetailPage.html?product=PER481-ELE |
Citation | IEEE Robotics and Automation Letters, 2021, v. 6 n. 2, p. 3317-3324 How to Cite? |
Abstract | This letter presents a computationally efficient and robust LiDAR-inertial odometry framework. We fuse LiDAR feature points with IMU data using a tightly-coupled iterated extended Kalman filter to allow robust navigation in fast-motion, noisy or cluttered environments where degeneration occurs. To lower the computation load in the presence of a large number of measurements, we present a new formula to compute the Kalman gain. The new formula has computation load depending on the state dimension instead of the measurement dimension. The proposed method and its implementation are tested in various indoor and outdoor environments. In all tests, our method produces reliable navigation results in real-time: running on a quadrotor onboard computer, it fuses more than 1200 effective feature points in a scan and completes all iterations of an iEKF step within 25 ms. Our codes are open-sourced on Github |
Persistent Identifier | http://hdl.handle.net/10722/301527 |
ISSN | 2023 Impact Factor: 4.6 2023 SCImago Journal Rankings: 2.119 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Xu, W | - |
dc.contributor.author | Zhang, F | - |
dc.date.accessioned | 2021-08-09T03:40:22Z | - |
dc.date.available | 2021-08-09T03:40:22Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | IEEE Robotics and Automation Letters, 2021, v. 6 n. 2, p. 3317-3324 | - |
dc.identifier.issn | 2377-3766 | - |
dc.identifier.uri | http://hdl.handle.net/10722/301527 | - |
dc.description.abstract | This letter presents a computationally efficient and robust LiDAR-inertial odometry framework. We fuse LiDAR feature points with IMU data using a tightly-coupled iterated extended Kalman filter to allow robust navigation in fast-motion, noisy or cluttered environments where degeneration occurs. To lower the computation load in the presence of a large number of measurements, we present a new formula to compute the Kalman gain. The new formula has computation load depending on the state dimension instead of the measurement dimension. The proposed method and its implementation are tested in various indoor and outdoor environments. In all tests, our method produces reliable navigation results in real-time: running on a quadrotor onboard computer, it fuses more than 1200 effective feature points in a scan and completes all iterations of an iEKF step within 25 ms. Our codes are open-sourced on Github | - |
dc.language | eng | - |
dc.publisher | Institute of Electrical and Electronics Engineers. The Journal's web site is located at https://www.ieee.org/membership-catalog/productdetail/showProductDetailPage.html?product=PER481-ELE | - |
dc.relation.ispartof | IEEE Robotics and Automation Letters | - |
dc.rights | IEEE Robotics and Automation Letters. Copyright © Institute of Electrical and Electronics Engineers. | - |
dc.rights | ©20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | - |
dc.subject | Aerial systems | - |
dc.subject | localization | - |
dc.subject | perception and autonomy | - |
dc.subject | sensor fusion | - |
dc.title | Fast-lio: A fast, robust lidar-inertial odometry package by tightly-coupled iterated Kalman filter | - |
dc.type | Article | - |
dc.identifier.email | Zhang, F: fuzhang@hku.hk | - |
dc.identifier.authority | Zhang, F=rp02460 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/LRA.2021.3064227 | - |
dc.identifier.scopus | eid_2-s2.0-85102545612 | - |
dc.identifier.hkuros | 324108 | - |
dc.identifier.volume | 6 | - |
dc.identifier.issue | 2 | - |
dc.identifier.spage | 3317 | - |
dc.identifier.epage | 3324 | - |
dc.identifier.isi | WOS:000633394300021 | - |
dc.publisher.place | United States | - |