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
本研究针对AUV传统DVL初始化流程耗时、依赖轨迹等缺陷，提出含ResAlignNet与DCNet的神经辅助统一初始化流程，仅需25秒近等速轨迹数据，使速度均方根误差平均降低68.7%。
AI 中文摘要
自主水下航行器（AUV）依靠惯性导航系统（INS）与多普勒速度计程仪（DVL）的融合实现精准导航。部署前，这种融合需要包含两个阶段的DVL初始化流程：对准阶段估计INS与DVL坐标系间的旋转关系，校准阶段估计DVL的误差项。传统方法中，两个阶段均采用基于模型的算法求解，要求复杂的航行器机动、水面卫星参考测量以及简化的误差模型，导致初始化耗时、依赖轨迹且对传感器质量敏感。本研究提出一种完全神经辅助的DVL初始化流程，用两个互补的神经网络替代上述两个阶段：用于对准的ResAlignNet和用于校准的DCNet。该统一流程可在原位运行，仅需一段近等速轨迹，且使用与基于模型基线相同的输入。通过在五种不同传感器误差项组合下采集的真实数据测试，所提流程相比基于模型的基线，将速度均方根误差平均降低68.7%，且仅需25秒数据即可完成初始化。
英文摘要
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.
