Human-assisted robotic detection of foreign object debris inside confined spaces of marine vessels using probabilistic mapping

Abstract

Many complex vehicular systems, such as large marine vessels, contain confined spaces like water tanks, which are critical for the safe functioning of the vehicles. It is particularly hazardous for humans to inspect such spaces due to limited accessibility, poor visibility, and unstructured configuration. While robots provide a viable alternative, they encounter the same set of challenges in realizing robust autonomy. In this work, we specifically address the problem of detecting foreign object debris (FODs) left inside the confined spaces using a visual mapping-based system that relies on Mahalanobis distance-driven comparisons between the nominal and online maps for local outlier identification. The identified outliers, corresponding to candidate FODs, are used to generate waypoints that are fed to a mobile ground robot to take camera photos. The photos are subsequently labeled by humans for final identification of the presence and types of FODs, leading to high detection accuracy while mitigating the effect of recall–precision tradeoff. Preliminary simulation studies, followed by extensive physical trials on a prototype tank, demonstrate the capability and potential of our FOD detection system.