水下机器人位置锁定应用中的在线目标跟踪器基准测试
Benchmarking Online Object Trackers for Underwater Robot Position Locking Applications
AI总结：
本文首次对七种以上基于机器学习的目标跟踪算法进行统一基准测试，用于ROV水下位置锁定，通过BlueROV2实验验证并开源数据集。
摘要：
自主控制遥控水下机器人（ROV）的位置对于广泛的水下工程应用至关重要，例如水下工业结构的检查和维护。因此，研究基于视觉的水下机器人导航与控制近年来受到越来越多的关注，以应对水下环境中面临的诸多挑战，如光照变化、浑浊度、相机图像畸变（由气泡引起）以及ROV位置扰动（由水下洋流引起）。在本文中，我们提出了（据我们所知）首个对七种以上基于机器学习（ML）的单目标跟踪算法进行严格统一基准测试的研究，用于ROV平台的基于视觉的位置锁定。我们提出了一种位置锁定系统，该系统处理ROV必须保持稳定所面对的目标物体的图像。然后，我们提出的系统使用不同目标跟踪算法的输出结果，自动纠正ROV在外部扰动下的位置。我们使用BlueROV2平台在室内泳池中进行了大量真实世界实验，并清晰展示了每种跟踪方法的优缺点。最后，为缓解水下ROV数据稀缺的问题，我们将采集的数据集作为开源发布，希望有益于未来的研究。
英文摘要：
Autonomously controlling the position of Remotely Operated underwater Vehicles (ROVs) is of crucial importance for a wide range of underwater engineering applications, such as in the inspection and maintenance of underwater industrial structures. Consequently, studying vision-based underwater robot navigation and control has recently gained increasing attention to counter the numerous challenges faced in underwater conditions, such as lighting variability, turbidity, camera image distortions (due to bubbles), and ROV positional disturbances (due to underwater currents). In this paper, we propose (to the best of our knowledge) a first rigorous unified benchmarking of more than seven Machine Learning (ML)-based one-shot object tracking algorithms for vision-based position locking of ROV platforms. We propose a position-locking system that processes images of an object of interest in front of which the ROV must be kept stable. Then, our proposed system uses the output result of different object tracking algorithms to automatically correct the position of the ROV against external disturbances. We conducted numerous real-world experiments using a BlueROV2 platform within an indoor pool and provided clear demonstrations of the strengths and weaknesses of each tracking approach. Finally, to help alleviate the scarcity of underwater ROV data, we release our acquired data base as open-source with the hope of benefiting future research.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
