Robotic three-dimensional imaging system for under-vehicle inspection

Abstract

We present our research efforts toward the deployment of 3-D sensing technology to an under-vehicle inspection robot. The 3-D sensing modality provides flexibility with ambient lighting and illumination in addition to the ease of visualization, mobility, and increased confidence toward inspection. We leverage laser-based range-imaging techniques to reconstruct the scene of interest and address various design challenges in the scene modeling pipeline. On these 3-D mesh models, we propose a curvature-based surface feature toward the interpretation of the reconstructed 3-D geometry. The curvature variation measure (CVM) that we define as the entropic measure of curvature quantifies surface complexity indicative of the information present in the surface. We are able to segment the digitized mesh models into smooth patches and represent the automotive scene as a graph network of patches. The CVM at the nodes of the graph describes the surface patch. We demonstrate the descriptiveness of the CVM on manufacturer CAD and laser-scanned models.