Wheel-Mounted/GNSS Fusion with AI-Aided Position Updates
It addresses the challenge of accurate and robust localization for autonomous ground vehicles, offering a practical improvement over existing wheel-mounted inertial/GNSS fusion approaches.
The paper proposes a hybrid neural inertial navigation framework that integrates wheel-mounted inertial sensors with GNSS updates, achieving a 46% reduction in position root mean squared error compared to standard fusion methods.
Accurate and robust localization remains a fundamental challenge for autonomous ground vehicles. In this work, we propose a hybrid neural inertial navigation framework that integrates a wheel-mounted inertial sensors, enforced periodic trajectories, and a simple, efficient neural network capable of regressing vehicle displacement with GNSS position updates in an error-state extended Kalman filter. The periodic trajectories increase the inertial signal-to-noise ratio, allowing the network to use only inertial readings to estimate displacement. The approach is validated through real-world experiments using multiple wheel-mounted inertial sensors. Experimental results demonstrate that the proposed method achieves a significant improvement in positioning accuracy, reducing the position root mean squared error by approximately 46 % compared to standard wheel-mounted inertial sensor fusion with GNSS updates.