Bayesian Learning-Enhanced Navigation with Deep Smoothing for Inertial-Aided Navigation

• URL: http://arxiv.org/abs/2603.25364v1
• Date: Thu, 26 Mar 2026 12:11:59 GMT
• Title: Bayesian Learning-Enhanced Navigation with Deep Smoothing for Inertial-Aided Navigation
• Abstract summary: BLENDS is a data-driven post-processing framework that augments the classical two-filter smoother with a transformer-based neural network.<n> BLENDS achieves horizontal position improvements of up to 63% over the baseline forward EKF.
• Score: 8.510813384980262
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate post-processing navigation is essential for applications such as survey and mapping, where the full measurement history can be exploited to refine past state estimates. Fixed-interval smoothing algorithms represent the theoretically optimal solution under Gaussian assumptions. However, loosely coupled INS/GNSS systems fundamentally inherit the systematic position bias of raw GNSS measurements, leaving a persistent accuracy gap that model-based smoothers cannot resolve. To address this limitation, we propose BLENDS, which integrates Bayesian learning with deep smoothing to enhance navigation performance. BLENDS is a a data-driven post-processing framework that augments the classical two-filter smoother with a transformer-based neural network. It learns to modify the filter covariance matrices and apply an additive correction to the smoothed error-state directly within the Bayesian framework. A novel Bayesian-consistent loss jointly supervises the smoothed mean and covariance, enforcing minimum-variance estimates while maintaining statistical consistency. BLENDS is evaluated on two real-world datasets spanning a mobile robot and a quadrotor. Across all unseen test trajectories, BLENDS achieves horizontal position improvements of up to 63% over the baseline forward EKF.

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