Surface shape description of 3D data from under vehicle inspection robot

Abstract

Our research efforts focus on the deployment of 3D sensing capabilities to a multi-modal under vehicle inspection robot. In this paper, we outline the various design challenges towards the automation of the 3D scene modeling task. We employ laser-based range imaging techniques to extract the geometry of a vehicle's undercarriage and present our results after range integration. We perform shape analysis on the digitized triangle mesh models by segmenting them into smooth surface patches based on the curvedness of the surface. Using a region-growing procedure, we then obtain the patch adjacency. On each of these patches, we apply our definition of the curvature variation measure (CVM) as a descriptor of surface shape complexity. We base the information-theoretic CVM on shape curvature, and extract shape information as the entropic measure of curvature to represent a component as a graph network of patches. The CVM at the nodes of the graph describe the surface patch. We then demonstrate our algorithm with results on automotive components. With apriori manufacturer information about the CAD models in the undercarriage we approach the technical challenge of threat detection with our surface shape description algorithm on the laser scanned geometry.