学习扫描哪一侧：面向自主水下航行器的侧扫声呐多视图知情主动感知
Learning Which Side to Scan: Multi-View Informed Active Perception with Side Scan Sonar for Autonomous Underwater Vehicles
AI总结：
针对自主水下航行器侧扫声呐勘测需兼顾效率与分类精度的问题，提出基于图表示和图神经网络的多视图主动感知框架，可自适应选择最优观测视角，在模拟实验中分类准确率和勘测效率均优于现有方法。
摘要：
自主水下航行器通常执行捕获目标多视图的勘测任务，以便为人类操作员或自动目标识别算法提供更多信息。在本研究中，我们解决了选择信息量最大的视图以在最小化勘测时间的同时最大化分类器准确率的问题。我们提出了一种新颖的主动感知框架，用于利用侧扫声呐图像进行多视图自适应勘测与目标重捕获。我们的框架通过为自适应勘测任务采用图公式化表示来应对这一挑战，随后使用图神经网络（Graph Neural Networks, GNNs）既对已获取的声呐视图进行分类，又基于收集的数据选择下一个最优视图。我们在高保真侧扫声呐模拟器中通过模拟勘测对所提方法进行了评估。结果表明，我们的方法在分类准确率和勘测效率上均能超越现有最优水平。该框架为涉及侧扫声呐的更高效自主任务（如水下勘探、海洋考古和环境监测）提供了一种有前景的方案。
英文摘要：
Autonomous underwater vehicles often perform surveys that capture multiple views of targets in order to provide more information for human operators or automatic target recognition algorithms. In this work, we address the problem of choosing the most informative views that minimize survey time while maximizing classifier accuracy. We introduce a novel active perception framework for multi-view adaptive surveying and reacquisition using side scan sonar imagery. Our framework addresses this challenge by using a graph formulation for the adaptive survey task. We then use Graph Neural Networks (GNNs) to both classify acquired sonar views and to choose the next best view based on the collected data. We evaluate our method using simulated surveys in a high-fidelity side scan sonar simulator. Our results demonstrate that our approach is able to surpass the state-of-the-art in classification accuracy and survey efficiency. This framework is a promising approach for more efficient autonomous missions involving side scan sonar, such as underwater exploration, marine archaeology, and environmental monitoring.
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。
