Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/91669
PIRA download icon_1.1View/Download Full Text
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 SizeFormat 
Zhang_Coarse-to-Fine_Loosely-Coupled_LiDAR-Inertial.pdf8.06 MBAdobe PDFView/Open
Open Access Information
Status open access
File Version Version of Record
Access
View full-text via PolyU eLinks SFX Query
Show full item record

Page views

101
Last Week
0
Last month
Citations as of Mar 24, 2024

Downloads

40
Citations as of Mar 24, 2024

WEB OF SCIENCETM
Citations

10
Citations as of Mar 28, 2024

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.