Abstract
This research is motivated towards the deployment of intelligent robots for under vehicle inspection at check-points, gate-entry terminals and parking lots. Using multi-modality measurements of temperature, range, color, radioactivity and with future potential for chemical and biological sensors, our approach is based on a modular robotic “sensor brick” architecture that integrates multi-sensor data into scene intelligence in 3D virtual reality environments. The remote 3D scene visualization capability reduces the risk on close-range inspection personnel, transforming the inspection task into an unmanned robotic mission. Our goal in this chapter is to focus on the 3D range “sensor brick” as a vital component in this multi-sensor robotics framework and demonstrate the potential of automatic threat detection using the geometric information from the 3D sensors. With the 3D data alone, we propose two different approaches for the detection of anomalous objects as potential threats. The first approach is to perform scene verification using a 3D registration algorithm for quickly and efficiently finding potential changes to the undercarriage by comparing previously archived scans of the same vehicle. The second 3D shape analysis approach assumes availability of the CAD models of the undercarriage that can be matched with the scanned real data using a novel perceptual curvature variation measure (CVM). The definition of the CVM, that can be understood as the entropy of surface curvature, describes the under vehicle scene as a graph network of smooth surface patches that readily lends to matching with the graph description of the aprioriCAD data. By presenting results of real-time acquisition, visualization, scene verification and description, we emphasize the scope of 3D imaging over several drawbacks with present day inspection systems using mirrors and 2D cameras.
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Sukumar, S.R., Page, D.L., Koschan, A.F., Abidi, M.A. (2007). Under Vehicle Inspection with 3d Imaging. In: Koschan, A., Pollefeys, M., Abidi, M. (eds) 3D Imaging for Safety and Security. Computational Imaging and Vision, vol 35. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-6182-0_11
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DOI: https://doi.org/10.1007/978-1-4020-6182-0_11
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