发表机构
University of Haifa（海法大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出NAViLoss，一种导航感知双残差损失，用于基于学习的AUV速度估计，通过联合惩罚导航状态和波束一致性残差并自适应调节不确定性，结合DeepONet架构，在海试数据上实现44%的精度提升。
AI 中文摘要
自主水下航行器（AUV）通常依赖由多普勒测速仪（DVL）辅助的惯性导航系统（INS）以实现可靠的水下导航。因此，准确的DVL速度估计对于成功作业至关重要。近年来，基于学习的方法在DVL速度估计方面显示出改进，尤其是在测量条件退化的情况下。然而，这些方法的训练目标通常依赖于对较大残差和损坏观测高度敏感的常规回归损失。此外，它们没有显式考虑底层传感过程相关的物理一致性和测量不确定性。为解决这些限制，本文引入导航感知损失（NAViLoss），一种用于基于学习的AUV速度估计的鲁棒且不确定性感知的目标函数。NAViLoss在导航状态域联合惩罚速度估计残差，并在DVL测量域惩罚波束一致性残差。其有界公式限制了大残差的影响，而自适应机制调节波束几何中的不确定性。此外，NAViLoss与DeepONet架构集成，形成一种新颖的NAVi-DeepONet模型，用于无缝估计水下航行器的速度。最后，我们使用在多次实际海试中收集的约10,000米半合成AUV实验数据评估我们的模型。实验结果表明，与常规和基于学习的基线相比，速度估计精度提高了44%。这些结果证明了导航感知和不确定性自适应损失设计对于鲁棒的基于学习的水下速度估计的有效性。
英文摘要
Autonomous underwater vehicles (AUVs) commonly rely on inertial navigation systems (INS) aided by Doppler velocity logs (DVLs) for reliable underwater navigation. Accurate DVL velocity estimation is therefore essential for successful operation. Recent learning-based methods have demonstrated improved DVL velocity estimation, particularly under degraded measurement conditions. However, their training objectives typically rely on conventional regression losses that are highly sensitive to large residuals and corrupted observations. Additionally, they do not explicitly account for the physical consistency and measurement uncertainty associated with the underlying sensing process. To address these limitations, this paper introduces navigation-aware loss (NAViLoss), a robust and uncertainty-aware objective function for learning-based AUV velocity estimation. NAViLoss jointly penalizes the velocity-estimation residual in the navigation-state domain and the beam-consistency residual in the DVL measurement domain. Its bounded formulation limits the influence of large residuals, while an adaptive mechanism regulates the uncertainty in beam geometry. Furthermore, NAViLoss is integrated with a DeepONet architecture to form a novel NAVi-DeepONet model for seamless estimation of an underwater vehicle's velocity. Lastly, our model is evaluated using approximately 10,000m of semi-synthetic AUV experimental data collected during multiple real-world sea trials. Experimental results demonstrate a 44% improvement in velocity-estimation accuracy compared with conventional and learning-based baselines. These results demonstrate the effectiveness of navigation-aware and uncertainty-adaptive loss design for robust learning-based underwater velocity estimation.
Comments26 pages, 7 figures
