Autonomous GPU-based UAS for inspection of confined spaces: Application to marine vessel classification

Abstract

Inspection of confined spaces poses a series of health risks to human surveyors, and therefore a need for robotic solutions arises. In this paper, we design and demonstrate a real-time system for collecting 3D structural and visual data from a series of inspection points within a prior map of a confined space. The system consists of a GPU accelerated 3D point cloud registration and a visual inertial odometry estimate fused in an Unscented Kalman Filter. Using the state-of-the-art deep learning-based feature descriptors, FCGF –and the robust Teaser++ 3D registration algorithm– point clouds from a narrow field of view, time-of-flight, camera can be registered to a prior map of the environment, to provide accurate cm-level absolute pose estimates. The uncertainty of the system is furthermore estimated on the basis of the novel GPU-based Stein ICP algorithm. Visual defects, represented by augmented reality fiducial markers, are automatically detected during inspection, and their positions are estimated in the map frame of the confined space. The performance of the system has been evaluated in realtime onboard a small UAV, within a mock-up model of a water ballast tank from a marine vessel, where the UAV was able to navigate and inspect the ambiguous and featureless environment. All defects were estimated within ＋/−10 cm of their actual position.