用深度学习改进惯性导航，显著提升定位精度。
Bayesian Learning-Enhanced Navigation with Deep Smoothing for Inertial-Aided Navigation
- 融合贝叶斯学习与Transformer网络，动态修正滤波协方差和误差状态。
- 在真实数据上实现水平定位精度最高提升63%。
- 适合需要高精度后处理的测绘、机器人导航场景。
高精度后处理导航对测绘等应用至关重要，可利用完整观测历史优化过去的状态估计。固定区间平滑算法在高斯假设下理论上最优，但松耦合的惯性/GNSS系统继承了原始GNSS测量中的系统性位置偏差，导致模型平滑器无法消除的精度瓶颈。为此，本文提出BLENDS，将贝叶斯学习与深度平滑结合，构建数据驱动的后处理框架。该框架在经典两滤波平滑基础上引入基于Transformer的神经网络，学习在贝叶斯框架内调整滤波协方差矩阵并直接施加附加误差校正。设计了一种新型贝叶斯一致损失函数，联合监督平滑均值与协方差，确保最小方差估计的同时保持统计一致性。在移动机器人和四旋翼无人机两个真实数据集上评估，所有未见测试轨迹中，水平位置精度相比基线前向扩展卡尔曼滤波（EKF）最高提升63%。
原文摘要 · Abstract (English)
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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