ベイズ学習と深層平滑化を統合した慣性航法の高精度化
Bayesian Learning-Enhanced Navigation with Deep Smoothing for Inertial-Aided Navigation
慣性/GNSS統合航法の後処理精度を高めるため、古典的な2フィルタ平滑化にTransformerベースのニューラルネットを組み合わせ、共分散行列の修正と平滑化誤差への補正をベイズ枠組み内で学習するフレームワークBLENDSを提案した。実データで最大63%の水平位置精度向上を確認。
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著者: Nadav Cohen, Itzik Klein
分類: cs.RO
原文アブストラクト
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.
