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INS/LIDAR/Stereo SLAM Integration for Precision Navigation in GNSS-Denied Environments
Traditionally, navigation systems have relied solely on global navigation satellite system (GNSS)/inertial navigation system (INS) integration. When a temporal loss of GNSS signal lock is encountered, these systems would rely on INS, which can sustain short bursts of outages, albeit drift significan...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10490647/ https://www.ncbi.nlm.nih.gov/pubmed/37687880 http://dx.doi.org/10.3390/s23177424 |
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author | Abdelaziz, Nader El-Rabbany, Ahmed |
author_facet | Abdelaziz, Nader El-Rabbany, Ahmed |
author_sort | Abdelaziz, Nader |
collection | PubMed |
description | Traditionally, navigation systems have relied solely on global navigation satellite system (GNSS)/inertial navigation system (INS) integration. When a temporal loss of GNSS signal lock is encountered, these systems would rely on INS, which can sustain short bursts of outages, albeit drift significantly in prolonged outages. In this study, an extended Kalman filter (EKF) is proposed to develop an integrated INS/LiDAR/Stereo simultaneous localization and mapping (SLAM) navigation system. The first update stage of the filter integrates the INS with the LiDAR, after which the resultant navigation solution is integrated with the stereo SLAM solution, which yields the final integrated navigation solution. The system was tested for different driving scenarios in urban and rural environments using the raw Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) dataset in the complete absence of the GNSS signal. In addition, the selected KITTI drives covered low and high driving speeds in feature-rich and feature-poor environments. It is shown that the proposed INS/LiDAR/Stereo SLAM navigation system yielded better position estimations in comparison to using the INS without any assistance from onboard sensors. The accuracy improvement was expressed as a reduction of the root-mean-square error (RMSE) by 83% and 82% in the horizontal and up directions, respectively. In addition, the proposed system outperformed the positioning accuracy of some of the state-of-the-art algorithms. |
format | Online Article Text |
id | pubmed-10490647 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-104906472023-09-09 INS/LIDAR/Stereo SLAM Integration for Precision Navigation in GNSS-Denied Environments Abdelaziz, Nader El-Rabbany, Ahmed Sensors (Basel) Article Traditionally, navigation systems have relied solely on global navigation satellite system (GNSS)/inertial navigation system (INS) integration. When a temporal loss of GNSS signal lock is encountered, these systems would rely on INS, which can sustain short bursts of outages, albeit drift significantly in prolonged outages. In this study, an extended Kalman filter (EKF) is proposed to develop an integrated INS/LiDAR/Stereo simultaneous localization and mapping (SLAM) navigation system. The first update stage of the filter integrates the INS with the LiDAR, after which the resultant navigation solution is integrated with the stereo SLAM solution, which yields the final integrated navigation solution. The system was tested for different driving scenarios in urban and rural environments using the raw Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) dataset in the complete absence of the GNSS signal. In addition, the selected KITTI drives covered low and high driving speeds in feature-rich and feature-poor environments. It is shown that the proposed INS/LiDAR/Stereo SLAM navigation system yielded better position estimations in comparison to using the INS without any assistance from onboard sensors. The accuracy improvement was expressed as a reduction of the root-mean-square error (RMSE) by 83% and 82% in the horizontal and up directions, respectively. In addition, the proposed system outperformed the positioning accuracy of some of the state-of-the-art algorithms. MDPI 2023-08-25 /pmc/articles/PMC10490647/ /pubmed/37687880 http://dx.doi.org/10.3390/s23177424 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 Abdelaziz, Nader El-Rabbany, Ahmed INS/LIDAR/Stereo SLAM Integration for Precision Navigation in GNSS-Denied Environments |
title | INS/LIDAR/Stereo SLAM Integration for Precision Navigation in GNSS-Denied Environments |
title_full | INS/LIDAR/Stereo SLAM Integration for Precision Navigation in GNSS-Denied Environments |
title_fullStr | INS/LIDAR/Stereo SLAM Integration for Precision Navigation in GNSS-Denied Environments |
title_full_unstemmed | INS/LIDAR/Stereo SLAM Integration for Precision Navigation in GNSS-Denied Environments |
title_short | INS/LIDAR/Stereo SLAM Integration for Precision Navigation in GNSS-Denied Environments |
title_sort | ins/lidar/stereo slam integration for precision navigation in gnss-denied environments |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10490647/ https://www.ncbi.nlm.nih.gov/pubmed/37687880 http://dx.doi.org/10.3390/s23177424 |
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