用Transformer模型提升水下机器人在长时间速度传感器失灵时的定位精度
Transformer-Based Robust Underwater Inertial Navigation in Prolonged Doppler Velocity Log Outages
- 基于Transformer的ST-BeamsNet融合惯性数据与历史声学速度信息预测航行速度
- 在长达50秒的完全失联情况下，速度误差降低63%，最终位置误差减少95%
- 适合水下探测、深海测绘等对导航可靠性要求高的场景
自主水下航行器（AUV）在海洋勘探、测绘等领域广泛应用，其导航系统依赖惯性传感器与多普勒测速仪（DVL）的数据融合，通常通过非线性滤波实现。DVL通过向海底发射声波并分析回波的多普勒频移来估算航行速度，但受环境影响，声束可能发生偏转或中断，导致信号丢失。在此类情况下，仅依靠惯性数据会引发累积误差，最终导致任务失败。为此，本文采用ST-BeamsNet这一深度学习方法，利用惯性数据与历史DVL数据，在短时失联期间估计速度。本工作进一步扩展该模型以应对长时间失联，并在扩展卡尔曼滤波框架下进行评估。实验表明，在长达50秒的完全DVL中断场景中，所提框架使速度均方根误差（RMSE）降低最多63%，最终位置误差减少最多95%。
原文摘要 · Abstract (English)
Autonomous underwater vehicles (AUV) have a wide variety of applications in the marine domain, including exploration, surveying, and mapping. Their navigation systems rely heavily on fusing data from inertial sensors and a Doppler velocity log (DVL), typically via nonlinear filtering. The DVL estimates the AUV's velocity vector by transmitting acoustic beams to the seabed and analyzing the Doppler shift from the reflected signals. However, due to environmental challenges, DVL beams can deflect or fail in real-world settings, causing signal outages. In such cases, the AUV relies solely on inertial data, leading to accumulated navigation errors and mission terminations. To cope with these outages, we adopted ST-BeamsNet, a deep learning approach that uses inertial readings and prior DVL data to estimate AUV velocity during isolated outages. In this work, we extend ST-BeamsNet to address prolonged DVL outages and evaluate its impact within an extended Kalman filter framework. Experiments demonstrate that the proposed framework improves velocity RMSE by up to 63% and reduces final position error by up to 95% compared to pure inertial navigation. This is in scenarios involving up to 50 seconds of complete DVL outage.
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