用神经网络统一解决水下机器人导航初始化难题，仅需25秒数据即可显著提效。
A Unified Neural-Aided Alignment and Calibration Method for AUVs
- 设计两个互补神经网络，替代传统复杂的手动校准流程。
- 在真实数据上平均降低68.7%速度均方根误差，仅用25秒完成初始化。
- 适合需要快速部署、低轨迹依赖的水下导航场景。
自主水下航行器（AUV）依赖惯性导航系统（INS）与多普勒速度计（DVL）融合实现精准定位。部署前需通过两阶段初始化流程：对齐（估计INS与DVL坐标系间的旋转）和校准（估计DVL误差项）。传统方法依赖模型驱动算法，需复杂机动、卫星参考及简化误差模型，导致初始化耗时长、轨迹依赖强且易受传感器质量影响。本文提出一种全神经辅助的统一初始化流程，以ResAlignNet完成对齐，以DCNet完成校准。该流程可在单条近似匀速航迹上原位运行，输入与传统基线一致。基于五组不同传感器误差组合的真实数据验证，所提方法在仅使用25秒数据的情况下，平均将速度均方根误差降低68.7%。
原文摘要 · Abstract (English)
Autonomous underwater vehicles (AUVs) rely on the fusion of inertial navigation systems (INS) and Doppler velocity logs (DVL) for accurate navigation. Before deployment, this fusion requires a DVL initialization pipeline consisting of two stages: alignment, which estimates the rotation between the INS and DVL frames, and calibration, which estimates the DVL error terms. Conventionally, both stages are solved with model-based algorithms that demand complex vehicle maneuvers, surface-level satellite reference measurements, and simplified error models, making initialization time-consuming, trajectory-dependent, and sensitive to sensor quality. In this work, we propose a fully neural- aided DVL initialization pipeline that replaces both stages with two complementary neural networks: ResAlignNet for alignment and DCNet for calibration. The unified pipeline operates in situ on a single nearly constant-velocity trajectory and uses the same inputs as the model-based baseline. Using real-world data recorded across five distinct sensor error-term combinations, the proposed pipeline reduces the velocity root mean squared error by an average of 68.7% over the model-based baseline, using only 25s of data for initialization.
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