BLENDS: 贝叶斯学习增强的深度平滑方法用于GNSS拒止环境
BLENDS: Bayesian Learning-Enhanced Deep Smoothing for GNSS-Denied Environments
AI总结：
提出BLENDS框架，结合贝叶斯平滑与Transformer学习，通过协方差自适应和状态校正提升GNSS拒止下的导航精度，在四旋翼数据集上位置RMSE改进达25.6%。
摘要：
在GNSS中断期间保持精确导航仍然是依赖低成本惯性传感器的自主系统面临的重大挑战。虽然经典平滑方法（如双滤波器平滑器和Rauch-Tung-Striebel平滑器）利用了中断前后收集的测量值，但其性能仍受限于传统GNSS测量的精度。本文提出贝叶斯学习增强的深度平滑导航（BLENDS），一种基于Transformer的框架，通过学习的协方差自适应和状态校正增强贝叶斯平滑。所提方法保留了贝叶斯估计的统计基础，同时利用数据驱动学习提高导航精度。在具有GNSS中断的四旋翼数据集上的评估表明，BLENDS始终优于基于模型的平滑器，位置均方根误差改进高达25.6%，同时降低了估计不确定性。此外，BLENDS学会了补偿传统GNSS定位与RTK地面真值之间的系统偏差，实现了超越单独使用传统GNSS测量所能达到的精度。结果展示了学习增强的贝叶斯平滑在GNSS受限环境中实现弹性高精度导航的潜力。
英文摘要：
Maintaining accurate navigation during GNSS outages remains a significant challenge for autonomous systems relying on low-cost inertial sensors. While classical smoothing methods, such as the two-filter smoother and Rauch-Tung-Striebel smoother, exploit measurements collected before and after an outage, their performance remains limited by the accuracy of conventional GNSS measurements. This paper presents Bayesian learning-enhanced navigation with deep smoothing (BLENDS), a transformer-based framework that augments Bayesian smoothing with learned covariance adaptation and state correction. The proposed method preserves the statistical foundations of Bayesian estimation while leveraging data-driven learning to improve navigation accuracy. Evaluations on the quadrotor dataset with GNSS outages demonstrate that BLENDS consistently outperforms both model-based smoothers, achieving up to 25.6% improvement in the position root mean square error while also reducing estimation uncertainty. Furthermore, BLENDS learns to compensate for the systematic bias between conventional GNSS positioning and RTK ground truth, enabling accuracy beyond that achievable with conventional GNSS measurements alone. The results demonstrate the potential of learning-enhanced Bayesian smoothing for resilient and high-accuracy navigation in GNSS-challenged environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
