arXiv：cs.LG· Arup Kumar Sahoo, Itzik Klein·· 4 小时前AI 评分31
NAViLoss：面向物理一致性学习的水下导航感知双残差目标函数
NAViLoss: An Underwater Navigation-Aware Dual-Residual Objective for Physics-Consistent Learning
AI 导读
研究人员提出导航感知损失函数 NAViLoss，联合惩罚导航状态域的速度估计残差与 DVL 测量域的波束一致性残差，并自适应调节波束几何不确定性，以提升 AUV 的 DVL 速度估计鲁棒性。该损失函数与 DeepONet 架构结合形成 NAVi-DeepONet 模型，在约 10,000m 半合成 AUV 海试数据上，速度估计精度较传统及学习基线提升 44%。
正文
Abstract: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.
| Comments: | 26 pages, 7 figures |
| Subjects: | Robotics (cs.RO); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.09690 [cs.RO] |
| (or arXiv:2610.09690v1 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2610.09690 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Arup Kumar Sahoo [view email]
[v1]
Wed, 7 Oct 2026 08:51:34 UTC (12,427 KB)
来源：arXiv：cs.LG · arxiv.org