Deep Learning-Aided Inertial/Visual/LiDAR Integration for GNSS-Challenging Environments
This research develops an integrated navigation system, which fuses the measurements of the inertial measurement unit (IMU), LiDAR, and monocular camera using an extended Kalman filter (EKF) to provide accurate positioning during prolonged GNSS signal outages. The system features the use of an integ...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/13/6019 |
| _version_ | 1797590784261750784 |
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| author | Nader Abdelaziz Ahmed El-Rabbany |
| author_facet | Nader Abdelaziz Ahmed El-Rabbany |
| author_sort | Nader Abdelaziz |
| collection | DOAJ |
| description | This research develops an integrated navigation system, which fuses the measurements of the inertial measurement unit (IMU), LiDAR, and monocular camera using an extended Kalman filter (EKF) to provide accurate positioning during prolonged GNSS signal outages. The system features the use of an integrated INS/monocular visual simultaneous localization and mapping (SLAM) navigation system that takes advantage of LiDAR depth measurements to correct the scale ambiguity that results from monocular visual odometry. The proposed system was tested using two datasets, namely, the KITTI and the Leddar PixSet, which cover a wide range of driving environments. The system yielded an average reduction in the root-mean-square error (RMSE) of about 80% and 92% in the horizontal and upward directions, respectively. The proposed system was compared with an INS/monocular visual SLAM/LiDAR SLAM integration and to some state-of-the-art SLAM algorithms. |
| first_indexed | 2024-03-11T01:29:21Z |
| format | Article |
| id | doaj.art-63f855ac51604278bae7984d9b9f9e44 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T01:29:21Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-63f855ac51604278bae7984d9b9f9e442023-11-18T17:30:12ZengMDPI AGSensors1424-82202023-06-012313601910.3390/s23136019Deep Learning-Aided Inertial/Visual/LiDAR Integration for GNSS-Challenging EnvironmentsNader Abdelaziz0Ahmed El-Rabbany1Department of Civil Engineering, Toronto Metropolitan University, Toronto, ON M5B 2K3, CanadaDepartment of Civil Engineering, Toronto Metropolitan University, Toronto, ON M5B 2K3, CanadaThis research develops an integrated navigation system, which fuses the measurements of the inertial measurement unit (IMU), LiDAR, and monocular camera using an extended Kalman filter (EKF) to provide accurate positioning during prolonged GNSS signal outages. The system features the use of an integrated INS/monocular visual simultaneous localization and mapping (SLAM) navigation system that takes advantage of LiDAR depth measurements to correct the scale ambiguity that results from monocular visual odometry. The proposed system was tested using two datasets, namely, the KITTI and the Leddar PixSet, which cover a wide range of driving environments. The system yielded an average reduction in the root-mean-square error (RMSE) of about 80% and 92% in the horizontal and upward directions, respectively. The proposed system was compared with an INS/monocular visual SLAM/LiDAR SLAM integration and to some state-of-the-art SLAM algorithms.https://www.mdpi.com/1424-8220/23/13/6019INS/LIMOINS/LIMO/LiDARintegrated navigation systemGNSS-denied environments |
| spellingShingle | Nader Abdelaziz Ahmed El-Rabbany Deep Learning-Aided Inertial/Visual/LiDAR Integration for GNSS-Challenging Environments Sensors INS/LIMO INS/LIMO/LiDAR integrated navigation system GNSS-denied environments |
| title | Deep Learning-Aided Inertial/Visual/LiDAR Integration for GNSS-Challenging Environments |
| title_full | Deep Learning-Aided Inertial/Visual/LiDAR Integration for GNSS-Challenging Environments |
| title_fullStr | Deep Learning-Aided Inertial/Visual/LiDAR Integration for GNSS-Challenging Environments |
| title_full_unstemmed | Deep Learning-Aided Inertial/Visual/LiDAR Integration for GNSS-Challenging Environments |
| title_short | Deep Learning-Aided Inertial/Visual/LiDAR Integration for GNSS-Challenging Environments |
| title_sort | deep learning aided inertial visual lidar integration for gnss challenging environments |
| topic | INS/LIMO INS/LIMO/LiDAR integrated navigation system GNSS-denied environments |
| url | https://www.mdpi.com/1424-8220/23/13/6019 |
| work_keys_str_mv | AT naderabdelaziz deeplearningaidedinertialvisuallidarintegrationforgnsschallengingenvironments AT ahmedelrabbany deeplearningaidedinertialvisuallidarintegrationforgnsschallengingenvironments |