Uncertainty-Aware Velocity Correction for Proprioceptive Vehicle Localization using Evidential Mamba
A learning-based architecture transforms onboard vehicle sensor data into a virtual velocity sensor for IMU drift correction without additional hardware. It uses a Mamba-based state space model and evidential deep learning for uncertainty quantification, achieving localization accuracy within 10% of a dedicated external sensor at 40 Hz on edge hardware.
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[Submitted on 6 Jul 2026]
Title:Uncertainty-Aware Velocity Correction for Proprioceptive Vehicle Localization using Evidential Mamba
View a PDF of the paper titled Uncertainty-Aware Velocity Correction for Proprioceptive Vehicle Localization using Evidential Mamba, by Abinav Kalyanasundaram and 2 other authors
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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.
Comments: Accepted at the 2026 International Conference on Indoor Positioning and Indoor Navigation (IPIN 2026), Rome, Italy. 6 pages, 4 figures
Subjects:
Robotics (cs.RO); Machine Learning (cs.LG)
Cite as: arXiv:2607.05669 [cs.RO]
(or arXiv:2607.05669v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.05669
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Abinav Kalyanasundaram [view email] [v1] Mon, 6 Jul 2026 22:28:24 UTC (2,012 KB)
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