{"ID":3005039,"CreatedAt":"2026-06-03T03:09:48.883664427Z","UpdatedAt":"2026-06-05T07:16:01.131756733Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.03265","arxiv_id":"2606.03265","title":"Wheel-Mounted/GNSS Fusion with AI-Aided Position Updates","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.","short_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 d...","url_abs":"https://arxiv.org/abs/2606.03265","url_pdf":"https://arxiv.org/pdf/2606.03265v1","authors":"[\"Gal Versano\",\"Itzik Klein\"]","published":"2026-06-02T07:27:05Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[]","has_code":false}