  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.
