Wheel-Mounted/GNSS Fusion with AI-Aided Position Updates

• URL: http://arxiv.org/abs/2606.03265v1
• Date: Tue, 02 Jun 2026 07:27:05 GMT
• Title: Wheel-Mounted/GNSS Fusion with AI-Aided Position Updates
• Abstract summary: 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 position updates in an error-state extended Kalman filter.<n> Experimental results demonstrate that the proposed method achieves a significant improvement in positioning accuracy, reducing the root mean squared error by approximately 46 % compared to standard wheel-mounted inertial sensor fusion with updates.
• Score: 5.5217350574838875
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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