Hallucinating Hidden Obstacles for Unmanned Surface Vehicles Using a Compositional Model

The water environment in which unmanned surface vehicles (USVs) navigate presents many unique challenges. One of these is the risk of encountering obstacles that are (partially) submerged and therefore poorly visible. Therefore, their extent cannot be determined directly from available above-water sensor data. On the other hand, it is well known that human skippers are able to safely navigate boats around obstacles even without underwater sensors and only with the help of their expertise. In this paper, we describe initial work on extending the USV obstacle detection to include such functionality using a compositional model. To learn to hallucinate the extent of obstacles with a minimum of learning effort, we exploit the nature of obstacles (people in kayaks, canoes, and on paddleboards) that are visible most of the time, but not always. We evaluate the impact of such hallucinations on USV safety and maneuverability, and suggest additional cases where such hallucinations can be used to improve USV safety.