发表机构
NTNU; Oceaneering AS（挪威科技大学; 奥星海洋公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出一种用于动态复杂水下环境中UUV的基于视觉的控制框架，可实现实时定位、导航与建图，经合成数据集验证及实船测试，性能实时且鲁棒，支持海洋机器人现场部署完成水下关键任务。
AI 中文摘要
本文提出了一种完全集成的基于视觉的框架，用于动态、视觉挑战性环境中无人水下航行器（UUV）的实时且鲁棒的定位、自主导航与建图。该提出的流程可实现净相对定位与全局定位，同时实时生成周围环境的连续三维地图。该框架在带有真值的合成数据集上得到验证，并在UUV上进行了自主净相对导航实验的实船测试。结果表明其具备实时性能与增强的鲁棒性，支持视觉驱动的自主导航，并使海洋机器人能够在复杂水下环境中执行关键巡检与建图任务的现场部署。
英文摘要
This paper presents a fully integrated vision-based framework for real-time and robust localization, autonomous navigation, and mapping for unmanned underwater vehicles (UUVs) in dynamic, visually challenging environments. The proposed pipeline enables both net-relative and global localization while generating continuous 3D maps of the surroundings in real-time. The framework was validated on synthetic datasets with ground truth and tested onboard an UUV during autonomous net-relative navigation experiments. Results demonstrate real-time performance and enhanced robustness, supporting vision-driven autonomous navigation and enabling the field deployment of marine robots for critical inspection and mapping tasks in complex underwater environments.
CommentsAccepted to IFAC WC 2026
