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Article: Fast-lio: A fast, robust lidar-inertial odometry package by tightly-coupled iterated Kalman filter

TitleFast-lio: A fast, robust lidar-inertial odometry package by tightly-coupled iterated Kalman filter
Authors
KeywordsAerial systems
localization
perception and autonomy
sensor fusion
Issue Date2021
PublisherInstitute 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?
AbstractThis 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 Identifierhttp://hdl.handle.net/10722/301527
ISSN
2023 Impact Factor: 4.6
2023 SCImago Journal Rankings: 2.119
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorXu, W-
dc.contributor.authorZhang, F-
dc.date.accessioned2021-08-09T03:40:22Z-
dc.date.available2021-08-09T03:40:22Z-
dc.date.issued2021-
dc.identifier.citationIEEE Robotics and Automation Letters, 2021, v. 6 n. 2, p. 3317-3324-
dc.identifier.issn2377-3766-
dc.identifier.urihttp://hdl.handle.net/10722/301527-
dc.description.abstractThis 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.languageeng-
dc.publisherInstitute 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.ispartofIEEE Robotics and Automation Letters-
dc.rightsIEEE 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.subjectAerial systems-
dc.subjectlocalization-
dc.subjectperception and autonomy-
dc.subjectsensor fusion-
dc.titleFast-lio: A fast, robust lidar-inertial odometry package by tightly-coupled iterated Kalman filter-
dc.typeArticle-
dc.identifier.emailZhang, F: fuzhang@hku.hk-
dc.identifier.authorityZhang, F=rp02460-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/LRA.2021.3064227-
dc.identifier.scopuseid_2-s2.0-85102545612-
dc.identifier.hkuros324108-
dc.identifier.volume6-
dc.identifier.issue2-
dc.identifier.spage3317-
dc.identifier.epage3324-
dc.identifier.isiWOS:000633394300021-
dc.publisher.placeUnited States-

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