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Journal of Sensors
Volume 2013 (2013), Article ID 832963, 16 pages
http://dx.doi.org/10.1155/2013/832963
Research Article

Rapid 3D Modeling and Parts Recognition on Automotive Vehicles Using a Network of RGB-D Sensors for Robot Guidance

Faculty of Engineering, School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada K1N 6N5

Received 15 March 2013; Revised 9 July 2013; Accepted 12 July 2013

Academic Editor: Lei Wang

Copyright © 2013 Alberto Chávez-Aragón et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper presents an approach for the automatic detection and fast 3D profiling of lateral body panels of vehicles. The work introduces a method to integrate raw streams from depth sensors in the task of 3D profiling and reconstruction and a methodology for the extrinsic calibration of a network of Kinect sensors. This sensing framework is intended for rapidly providing a robot with enough spatial information to interact with automobile panels using various tools. When a vehicle is positioned inside the defined scanning area, a collection of reference parts on the bodywork are automatically recognized from a mosaic of color images collected by a network of Kinect sensors distributed around the vehicle and a global frame of reference is set up. Sections of the depth information on one side of the vehicle are then collected, aligned, and merged into a global RGB-D model. Finally, a 3D triangular mesh modelling the body panels of the vehicle is automatically built. The approach has applications in the intelligent transportation industry, automated vehicle inspection, quality control, automatic car wash systems, automotive production lines, and scan alignment and interpretation.