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

2026-06-02

AI Summary

This study addresses the challenge of achieving high-precision and robust localization for autonomous vehicles in GNSS-denied or degraded environments. The authors propose a novel approach that fuses wheel-mounted inertial sensing, periodic trajectory constraints, and a lightweight neural network. By incorporating prior knowledge of periodic motion into an error-state extended Kalman filter (ES-EKF) framework, the method enhances the signal-to-noise ratio of inertial measurements. Additionally, an efficient neural network is employed to regress displacement from pure inertial data, significantly improving localization accuracy during GNSS outages. Experimental results demonstrate that the proposed method reduces root-mean-square positioning error by approximately 46% compared to conventional wheel-mounted inertial–GNSS fusion schemes, thereby substantially enhancing both accuracy and robustness.

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.

Problem

• localization
• autonomous ground vehicles
• positioning accuracy
• inertial navigation
• GNSS

Innovation

• neural inertial navigation
• wheel-mounted IMU
• GNSS fusion
• periodic trajectory
• error-state EKF