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Deep Learning-Aided Inertial/Visual/LiDAR Integration for GNSS-Challenging Environments.

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This study presents an integrated navigation system fusing inertial measurement unit (IMU), LiDAR, and camera data for accurate positioning during GPS outages. The system significantly reduces positioning errors, improving navigation reliability.

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Area of Science:

  • Robotics and Autonomous Systems
  • Navigation and Positioning Technology
  • Sensor Fusion

Background:

  • Global Navigation Satellite System (GNSS) signal outages pose significant challenges for autonomous vehicle navigation.
  • Existing systems often struggle with accuracy and reliability during prolonged GNSS denial.
  • Accurate localization is critical for safe and efficient operation of autonomous systems.

Purpose of the Study:

  • To develop and evaluate an integrated navigation system for robust positioning during GNSS outages.
  • To fuse data from Inertial Measurement Unit (IMU), LiDAR, and monocular camera using an Extended Kalman Filter (EKF).
  • To address scale ambiguity in monocular visual odometry using LiDAR depth measurements.

Main Methods:

  • Development of an integrated Inertial Navigation System (INS)/monocular visual Simultaneous Localization and Mapping (SLAM) system.
  • Fusion of IMU, LiDAR, and monocular camera measurements via an Extended Kalman Filter (EKF).
  • Utilizing LiDAR depth data to resolve scale ambiguity inherent in monocular visual odometry.

Main Results:

  • Achieved an average reduction in root-mean-square error (RMSE) of approximately 80% horizontally and 92% vertically.
  • Demonstrated superior performance compared to INS/monocular visual SLAM/LiDAR SLAM integration.
  • Validated system performance across diverse driving scenarios using KITTI and Leddar PixSet datasets.

Conclusions:

  • The proposed integrated navigation system offers highly accurate and reliable positioning during GNSS signal outages.
  • The fusion strategy effectively leverages multi-sensor data, overcoming limitations of individual sensors.
  • This approach significantly enhances the robustness of autonomous navigation systems.