Zhu, H., Qiu, Y., Li, Y. et al. (2 more authors) (2024) An adaptive multi-sensor fusion for intelligent vehicle localization. IEEE Sensors Journal, 24 (6). pp. 8798-8806. ISSN 1530-437X
Abstract
Localization is a basic technology for intelligent vehicle (IV), which is usually carried out by fusing multiple sensors. In order to achieve robust and accurate localization results, a novel adaptive multi-sensor fusion method is proposed. For each sensor, every measurement is identified by an indicator, which is used to recognize whether the measurement has the useful information to improve the localization performance. A robust localization model of IV is then developed by using variational Bayesian approach. Simulations and experiments using a real IV are used to demonstrate the potential and effectiveness of the proposed method.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 The Author(s). Except as otherwise noted, this author-accepted version of a journal article published in IEEE Sensors Journal is made available via the University of Sheffield Research Publications and Copyright Policy under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ |
Keywords: | Localization; intelligent vehicle; sensor fusion; variational Bayesian |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 01 Feb 2024 15:52 |
Last Modified: | 07 Nov 2024 12:00 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Refereed: | Yes |
Identification Number: | 10.1109/JSEN.2024.3360083 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:208373 |
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Filename: IEEE Sensors 2024 Intelligent Vehicle Localization.pdf
Licence: CC-BY 4.0