Informative Multiview Planning for Underwater Sensors

Many modern imaging sensors must obtain multiple looks or “views” of a target at different orientations to automatically classify it with high confidence. Therefore, when tasked with classifying many targets, a mobile sensor may need to travel a long distance to change its position and orientation relative to every target, resulting in costly and time-consuming operations. This article presents a novel and general approach, referred to as informative multiview planning (IMVP) that simultaneously determines the most informative sequence of views and the shortest path between them. The approach is demonstrated on an underwater multitarget classification problem in which a sidescan sonar installed on an unmanned underwater vehicle must classify all targets in minimum time. Simulation and experimental results show that IMVP can achieve the same, or better, classification performance in half the time of existing multiview path planning methods. Also, by determining the most informative views and the shortest path between them, IMVP significantly improves classification efficiency, classification confidence level, as well as performance robustness.