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.

翻译：自主水下航行器在执行调查任务时，通常需要捕获目标的多个视角，以便为人类操作员或自动目标识别算法提供更多信息。本研究探讨如何选择最具信息量的视角，以在最大化分类器准确率的同时最小化调查时间。我们提出了一种新颖的主动感知框架，用于基于侧扫声纳图像的多视角自适应调查与重新捕获。该框架通过图建模方式处理自适应调查任务，并利用图神经网络（GNNs）对获取的声纳视角进行分类，同时根据已收集数据选择下一个最佳视角。我们在高保真侧扫声纳模拟器中通过仿真调查评估了该方法。实验结果表明，我们的方法在分类准确率和调查效率上均超越了现有最优技术。该框架为涉及侧扫声纳的高效自主任务（如水下探测、海洋考古与环境监测）提供了具有前景的解决方案。