Human-Assisted Robotic Detection of Foreign Object Debris Inside Confined Spaces of Marine Vessels Using Probabilistic Mapping

TL;DR

This paper presents a human-assisted robotic system for detecting foreign object debris inside confined spaces of marine vessels, combining probabilistic mapping, remote human verification, and physical robot experiments to improve safety and reliability.

Contribution

It introduces a novel visual mapping-based FOD detection method using Mahalanobis distance and integrates human assistance to enhance detection precision in confined marine environments.

Findings

High recall in outlier detection from simulation trials

Human assistance improves detection precision

Feasibility demonstrated on a GPU-enabled robot platform

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. 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.