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INS/LIDAR/Stereo SLAM Integration for Precision Navigation in GNSS-Denied Environments
Nader Abdelaziz1,2, Ahmed El-Rabbany1
1Department of Civil Engineering, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada.
Sensors (Basel, Switzerland)
|September 9, 2023
Summary
This study introduces an integrated navigation system combining inertial navigation (INS), LiDAR, and stereo vision (SLAM) to overcome global navigation satellite system (GNSS) signal loss. The system significantly improves positioning accuracy without GNSS, outperforming traditional methods.
Area of Science:
- Robotics
- Computer Vision
- Navigation Systems
Background:
- Traditional navigation relies on Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS) integration.
- INS alone suffers from significant drift during prolonged GNSS signal outages.
- Need for robust navigation solutions independent of GNSS signals.
Purpose of the Study:
- To develop an integrated INS/LiDAR/Stereo Simultaneous Localization and Mapping (SLAM) navigation system.
- To evaluate the system's performance in GNSS-denied environments.
- To enhance positioning accuracy compared to INS-only systems.
Main Methods:
- An extended Kalman filter (EKF) was employed for sensor fusion.
- The EKF integrates INS with LiDAR data first, followed by stereo SLAM.
- The system was tested on the KITTI dataset across diverse urban and rural driving scenarios.
Main Results:
- The proposed INS/LiDAR/Stereo SLAM system demonstrated superior position estimation without GNSS.
- Root-mean-square error (RMSE) was reduced by 83% horizontally and 82% vertically.
- The system outperformed several state-of-the-art algorithms in positioning accuracy.
Conclusions:
- Integrated INS/LiDAR/Stereo SLAM offers a robust solution for GNSS-denied navigation.
- The proposed EKF-based fusion significantly enhances localization accuracy.
- This approach provides a viable alternative for reliable navigation in challenging environments.
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