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An Integrated INS/LiDAR SLAM Navigation System for GNSS-Challenging Environments

Traditional navigation systems rely on GNSS/inertial navigation system (INS) integration, in which the INS can provide reliable positioning during short GNSS outages. However, if the GNSS outage persists for prolonged periods of time, the performance of the system will be solely dependent on the INS...

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Detalles Bibliográficos
Autores principales: Abdelaziz, Nader, El-Rabbany, Ahmed
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9227473/
https://www.ncbi.nlm.nih.gov/pubmed/35746108
http://dx.doi.org/10.3390/s22124327
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author Abdelaziz, Nader
El-Rabbany, Ahmed
author_facet Abdelaziz, Nader
El-Rabbany, Ahmed
author_sort Abdelaziz, Nader
collection PubMed
description Traditional navigation systems rely on GNSS/inertial navigation system (INS) integration, in which the INS can provide reliable positioning during short GNSS outages. However, if the GNSS outage persists for prolonged periods of time, the performance of the system will be solely dependent on the INS, which can lead to a significant drift over time. As a result, the need to integrate additional onboard sensors is essential. This study proposes a robust loosely coupled (LC) integration between the INS and LiDAR simultaneous mapping and localization (SLAM) using an extended Kalman filter (EKF). The proposed integrated navigation system was tested for three different driving scenarios and environments using the raw KITTI dataset. The first scenario used the KITTI residential datasets, totaling 48 min, while the second case study considered the KITTI highway datasets, totaling 7 min. For both case studies, a complete absence of the GNSS signal was assumed for the whole trajectory of the vehicle in all drives. In contrast, the third case study considered the use of minimal assistance from GNSS, which mimics the intermittent receipt and loss of GNSS signals for different driving environments. The positioning results of the proposed INS/LiDAR SLAM integrated system outperformed the performance of the INS for the residential datasets with an average reduction in the root mean square error (RMSE) in the horizontal and up directions of 88% and 32%, respectively. For the highway datasets, the RMSE reductions were 70% and 0.2% for the horizontal and up directions, respectively.
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spelling pubmed-92274732022-06-25 An Integrated INS/LiDAR SLAM Navigation System for GNSS-Challenging Environments Abdelaziz, Nader El-Rabbany, Ahmed Sensors (Basel) Article Traditional navigation systems rely on GNSS/inertial navigation system (INS) integration, in which the INS can provide reliable positioning during short GNSS outages. However, if the GNSS outage persists for prolonged periods of time, the performance of the system will be solely dependent on the INS, which can lead to a significant drift over time. As a result, the need to integrate additional onboard sensors is essential. This study proposes a robust loosely coupled (LC) integration between the INS and LiDAR simultaneous mapping and localization (SLAM) using an extended Kalman filter (EKF). The proposed integrated navigation system was tested for three different driving scenarios and environments using the raw KITTI dataset. The first scenario used the KITTI residential datasets, totaling 48 min, while the second case study considered the KITTI highway datasets, totaling 7 min. For both case studies, a complete absence of the GNSS signal was assumed for the whole trajectory of the vehicle in all drives. In contrast, the third case study considered the use of minimal assistance from GNSS, which mimics the intermittent receipt and loss of GNSS signals for different driving environments. The positioning results of the proposed INS/LiDAR SLAM integrated system outperformed the performance of the INS for the residential datasets with an average reduction in the root mean square error (RMSE) in the horizontal and up directions of 88% and 32%, respectively. For the highway datasets, the RMSE reductions were 70% and 0.2% for the horizontal and up directions, respectively. MDPI 2022-06-07 /pmc/articles/PMC9227473/ /pubmed/35746108 http://dx.doi.org/10.3390/s22124327 Text en © 2022 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
An Integrated INS/LiDAR SLAM Navigation System for GNSS-Challenging Environments
title An Integrated INS/LiDAR SLAM Navigation System for GNSS-Challenging Environments
title_full An Integrated INS/LiDAR SLAM Navigation System for GNSS-Challenging Environments
title_fullStr An Integrated INS/LiDAR SLAM Navigation System for GNSS-Challenging Environments
title_full_unstemmed An Integrated INS/LiDAR SLAM Navigation System for GNSS-Challenging Environments
title_short An Integrated INS/LiDAR SLAM Navigation System for GNSS-Challenging Environments
title_sort integrated ins/lidar slam navigation system for gnss-challenging environments
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9227473/
https://www.ncbi.nlm.nih.gov/pubmed/35746108
http://dx.doi.org/10.3390/s22124327
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