Wheel-Mounted/GNSS Fusion with AI-Aided Position Updates
Summary
arXiv:2606.03265v1 Announce Type: new 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.
Why It Matters
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Key Facts
- SectorEnergy
- Market—
- ImpactLow (42/100)
- SignalResearch