Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/91669
| Title: | Coarse-to-fine loosely-coupled lidar-inertial odometry for urban positioning and mapping | Authors: | Zhang, JC Wen, WS Huang, F Chen, XD Hsu, LT |
Issue Date: | 2021 | Source: | Remote sensing, 2021, v. 13, no. 12, 2371 | Abstract: | Accurate positioning and mapping are significant for autonomous systems with navigation requirements. In this paper, a coarse-to-fine loosely-coupled (LC) LiDAR-inertial odometry (LC-LIO) that could explore the complementariness of LiDAR and inertial measurement unit (IMU) was proposed for the real-time and accurate pose estimation of a ground vehicle in urban environments. Different from the existing tightly-coupled (TC) LiDAR-inertial fusion schemes which directly use all the considered ranges and inertial measurements to optimize the vehicle pose, the method proposed in this paper performs loosely-couped integrated optimization with the high-frequency motion prediction, which was produced by IMU integration based on optimized results, employed as the initial guess of LiDAR odometry to approach the optimality of LiDAR scan-to-map registration. As one of the prominent contributions, thorough studies were conducted on the performance upper bound of the TC LiDAR-inertial fusion schemes and LC ones, respectively. Furthermore, the experimental verification was performed on the proposition that the proposed pipeline can fully relax the potential of the LiDAR measurements (centimeter-level ranging accuracy) in a coarse-to-fine way without being disturbed by the unexpected IMU bias. Moreover, an adaptive covariance estimation method employed during LC optimization was proposed to explain the uncertainty of LiDAR scan-to-map registration in dynamic scenarios. Furthermore, the effectiveness of the proposed system was validated on challenging real-world datasets. Meanwhile, the process that the proposed pipelines realized the coarse-to-fine LiDAR scan-to-map registration was presented in detail. Comparing with the existing state-of-the-art TC-LIO, the focus of this study would be placed on that the proposed LC-LIO work could achieve similar or better accuracy with a reduced computational expense. | Keywords: | LiDAR-inertial odometry Loosely-coupled integration Adaptive covariance estimation Positioning Mapping Autonomous systems Urban canyons |
Publisher: | Molecular Diversity Preservation International (MDPI) | Journal: | Remote sensing | EISSN: | 2072-4292 | DOI: | 10.3390/rs13122371 | Rights: | Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). The following publication Zhang, J.; Wen, W.; Huang, F.; Chen, X.; Hsu, L.-T. Coarse-to-Fine Loosely-Coupled LiDAR-Inertial Odometry for Urban Positioning and Mapping. Remote Sens. 2021, 13, 2371 is available at https://doi.org/10.3390/rs13122371 |
| Appears in Collections: | Journal/Magazine Article |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Zhang_Coarse-to-Fine_Loosely-Coupled_LiDAR-Inertial.pdf | 8.06 MB | Adobe PDF | View/Open |
Access
View full-text via PolyU eLinks 
Page views
61
Last Week
0
0
Last month
Citations as of May 28, 2023
Downloads
25
Citations as of May 28, 2023
WEB OF SCIENCETM
Citations
7
Citations as of Jun 1, 2023
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.