  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. Simulation trials show extremely high recall but low
precision for the outlier identification method. The assistance of remote
humans is, therefore, taken to deal with the precision problem by going over
the close-up robot camera images of the outlier regions. An online survey is
conducted to show the usefulness of this assistance process. Physical
experiments are also reported on a GPU-enabled mobile robot platform inside a
scaled-down, prototype tank to demonstrate the feasibility of the FOD detection
system.
