用深度学习加速水下机器人多普勒测速仪校准，快70%、准80%。
DCNet: A Data-Driven Framework for DVL Calibration
- 设计双卷积神经网络，基于恒定速度轨迹实现快速校准。
- 实测提升70%定位精度，校准时间缩短80%。
- 适合低成本高精度水下导航系统，推动海洋机器人普及。
自主水下航行器（AUV）依赖惯性传感器与多普勒测速仪（DVL）融合进行导航，其中DVL提供精确的速度更新。为确保导航准确，任务前需对DVL进行校准以估计误差项。传统方法依赖复杂轨迹和非线性滤波器，耗时较长。本文提出DCNet——一种数据驱动的校准框架，创新性地使用二维卷积核处理数据。结合新提出的DVL误差模型，该方法可在近似恒定速度轨迹上实现快速校准。基于276分钟真实DVL数据集训练与测试，结果表明：相比基线方法，平均定位精度提升70%，校准时间缩短80%，即使在低性能DVL下也表现优异。该成果使低成本DVL可实现高精度、短时校准，拓展了低成本高精度水下导航的应用前景。
原文摘要 · Abstract (English)
Autonomous underwater vehicles (AUVs) are underwater robotic platforms used in a variety of applications. An AUV's navigation solution relies heavily on the fusion of inertial sensors and Doppler velocity logs (DVL), where the latter delivers accurate velocity updates. To ensure accurate navigation, a DVL calibration is undertaken before the mission begins to estimate its error terms. During calibration, the AUV follows a complex trajectory and employs nonlinear estimation filters to estimate error terms. In this paper, we introduce DCNet, a data-driven framework that utilizes a two-dimensional convolution kernel in an innovative way. Using DCNet and our proposed DVL error model, we offer a rapid calibration procedure. This can be applied to a trajectory with a nearly constant velocity. To train and test our proposed approach a dataset of 276 minutes long with real DVL recorded measurements was used. We demonstrated an average improvement of 70% in accuracy and 80% improvement in calibration time, compared to the baseline approach, with a low-performance DVL. As a result of those improvements, an AUV employing a low-cost DVL, can achieve higher accuracy, shorter calibration time, and apply a simple nearly constant velocity calibration trajectory. Our results also open up new applications for marine robotics utilizing low-cost, high-accurate DVLs.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。