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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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/12/4327 |
| _version_ | 1797482553666437120 |
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| author | Nader Abdelaziz Ahmed El-Rabbany |
| author_facet | Nader Abdelaziz Ahmed El-Rabbany |
| author_sort | Nader Abdelaziz |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T22:34:04Z |
| format | Article |
| id | doaj.art-aa887c2af6b143f3bd411d1b4516a436 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T22:34:04Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-aa887c2af6b143f3bd411d1b4516a4362023-11-23T18:51:38ZengMDPI AGSensors1424-82202022-06-012212432710.3390/s22124327An Integrated INS/LiDAR SLAM Navigation System for GNSS-Challenging EnvironmentsNader Abdelaziz0Ahmed El-Rabbany1Department of Civil Engineering, Ryerson University, Toronto, ON M5B 2K3, CanadaDepartment of Civil Engineering, Ryerson University, Toronto, ON M5B 2K3, CanadaTraditional 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.https://www.mdpi.com/1424-8220/22/12/4327Kitware SLAMINS/LiDAR SLAM integrationintegrated navigation system |
| spellingShingle | Nader Abdelaziz Ahmed El-Rabbany An Integrated INS/LiDAR SLAM Navigation System for GNSS-Challenging Environments Sensors Kitware SLAM INS/LiDAR SLAM integration integrated navigation system |
| 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 | Kitware SLAM INS/LiDAR SLAM integration integrated navigation system |
| url | https://www.mdpi.com/1424-8220/22/12/4327 |
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