Uncertainty-Aware Velocity Correction for Proprioceptive Vehicle Localization using Evidential Mamba

• URL: http://arxiv.org/abs/2607.05669v1
• Date: Mon, 06 Jul 2026 22:28:24 GMT
• Title: Uncertainty-Aware Velocity Correction for Proprioceptive Vehicle Localization using Evidential Mamba
• Abstract summary: Existing approaches provide drift correction through dedicated infrastructure, expensive external sensors, or complex multi-sensor fusion.<n>We propose Evidential Velocity Correction using Mamba, a learning-based architecture that transforms onboard vehicle sensor data into a virtual velocity sensor for IMU drift correction without additional hardware.<n> Evaluation on real-world vehicle data demonstrates that the proposed velocity correction localization accuracy within 10% of a dedicated external velocity sensor across different outage durations.
• Score: 17.576449361552992
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reliable localization in GNSS-denied environments remains a fundamental challenge for intelligent vehicles, as inertial navigation systems accumulate unbounded drift without external correction. Existing approaches provide drift correction through dedicated infrastructure, expensive external sensors, or complex multi-sensor fusion, each introducing practical deployment barriers. We propose Evidential Velocity Correction using Mamba (EVC-Mamba), a learning-based architecture that transforms onboard vehicle sensor data into a virtual velocity sensor for IMU drift correction without additional hardware. A Mamba-based selective state space model captures the temporal dynamics of vehicle motion, while evidential deep learning with a Normal-Inverse-Gamma distribution provides principled uncertainty quantification. The resulting uncertainty-aware velocity estimate is incorporated as a virtual correction measurement into an Error-State Extended Kalman Filter to reduce position drift. Evaluation on real-world vehicle data demonstrates that inertial navigation using the proposed velocity correction achieves localization accuracy within 10% of a dedicated external velocity sensor across different outage durations. The proposed architecture supports real-time onboard deployment at 40 Hz on edge hardware, enabling reliable localization during prolonged GNSS outages.

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