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A Robust INS/USBL/DVL Integrated Navigation Algorithm Using Graph Optimization

The Autonomous Underwater Vehicle (AUV) is usually equipped with multiple sensors, such as an inertial navigation system (INS), ultra-short baseline system (USBL), and Doppler velocity log (DVL), to achieve autonomous navigation. Multi-source information fusion is the key to realizing high-precision...

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Detalles Bibliográficos
Autores principales: Li, Peijuan, Liu, Yiting, Yan, Tingwu, Yang, Shutao, Li, Rui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9864396/
https://www.ncbi.nlm.nih.gov/pubmed/36679713
http://dx.doi.org/10.3390/s23020916
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author Li, Peijuan
Liu, Yiting
Yan, Tingwu
Yang, Shutao
Li, Rui
author_facet Li, Peijuan
Liu, Yiting
Yan, Tingwu
Yang, Shutao
Li, Rui
author_sort Li, Peijuan
collection PubMed
description The Autonomous Underwater Vehicle (AUV) is usually equipped with multiple sensors, such as an inertial navigation system (INS), ultra-short baseline system (USBL), and Doppler velocity log (DVL), to achieve autonomous navigation. Multi-source information fusion is the key to realizing high-precision underwater navigation and positioning. To solve the problem, a fusion scheme based on factor graph optimization (FGO) is proposed. Due to multiple iterations and joint optimization of historical data, FGO could usually show a better performance than the traditional Kalman filter. In addition, considering that USBL and DVL are usually heavily influenced by the environment, outliers are often present. A robust integrated navigation algorithm based on a maximum correntropy criterion and FGO scheme is proposed. The proposed algorithm solves the problem of multi-sensor fusion and non-Gaussian noise. Numerical simulations and field tests demonstrate that the proposed FGO scheme shows a better performance and robustness than the traditional Kalman filter. Compared with the traditional Kalman filtering, the positioning accuracy is improved by 5.3%, 9.1%, and 5.1% in the east, north, and height directions. It can realize a more accurate navigation and positioning of underwater multi-sensors.
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spelling pubmed-98643962023-01-22 A Robust INS/USBL/DVL Integrated Navigation Algorithm Using Graph Optimization Li, Peijuan Liu, Yiting Yan, Tingwu Yang, Shutao Li, Rui Sensors (Basel) Article The Autonomous Underwater Vehicle (AUV) is usually equipped with multiple sensors, such as an inertial navigation system (INS), ultra-short baseline system (USBL), and Doppler velocity log (DVL), to achieve autonomous navigation. Multi-source information fusion is the key to realizing high-precision underwater navigation and positioning. To solve the problem, a fusion scheme based on factor graph optimization (FGO) is proposed. Due to multiple iterations and joint optimization of historical data, FGO could usually show a better performance than the traditional Kalman filter. In addition, considering that USBL and DVL are usually heavily influenced by the environment, outliers are often present. A robust integrated navigation algorithm based on a maximum correntropy criterion and FGO scheme is proposed. The proposed algorithm solves the problem of multi-sensor fusion and non-Gaussian noise. Numerical simulations and field tests demonstrate that the proposed FGO scheme shows a better performance and robustness than the traditional Kalman filter. Compared with the traditional Kalman filtering, the positioning accuracy is improved by 5.3%, 9.1%, and 5.1% in the east, north, and height directions. It can realize a more accurate navigation and positioning of underwater multi-sensors. MDPI 2023-01-12 /pmc/articles/PMC9864396/ /pubmed/36679713 http://dx.doi.org/10.3390/s23020916 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/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/).
spellingShingle Article
Li, Peijuan
Liu, Yiting
Yan, Tingwu
Yang, Shutao
Li, Rui
A Robust INS/USBL/DVL Integrated Navigation Algorithm Using Graph Optimization
title A Robust INS/USBL/DVL Integrated Navigation Algorithm Using Graph Optimization
title_full A Robust INS/USBL/DVL Integrated Navigation Algorithm Using Graph Optimization
title_fullStr A Robust INS/USBL/DVL Integrated Navigation Algorithm Using Graph Optimization
title_full_unstemmed A Robust INS/USBL/DVL Integrated Navigation Algorithm Using Graph Optimization
title_short A Robust INS/USBL/DVL Integrated Navigation Algorithm Using Graph Optimization
title_sort robust ins/usbl/dvl integrated navigation algorithm using graph optimization
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9864396/
https://www.ncbi.nlm.nih.gov/pubmed/36679713
http://dx.doi.org/10.3390/s23020916
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