Imaging sonar based active viewpoint adjustment for underwater SLAM in structured environments

Simultaneous localization and mapping (SLAM) is a critical capability for any autonomous underwater vehicle (AUV) in various underwater applications, including infrastructure inspection and seabed exploration. However, achieving robust and accurate state estimation in such environments remains a significant hurdle. This is primarily attributable to the inherent scarcity of geometric features in the subsea environment itself and the limited field of view (FoV) of imaging sonar used for feature acquisition. These factors collectively elevate the probability of iterative closest point (ICP) degeneracy, a frequent challenge in most SLAM solutions. To overcome this limitation, this study proposes a method that actively adjusts the imaging sonar