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Proceedings Paper

Application of a reflectance model to the sensor planning system
Author(s): Tomáš Koutecký; David Paloušek; Jan Brandejs
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Paper Abstract

This study describes a new sensor planning system for the automatic generation of scanning positions based on a computer model of the part for digitization of sheet metal parts. The focus of this paper is in the application of a reflectance model into this sensor planning system. The goal of this sensor planning system and application of this model is to ensure fast, complete and accurate digitization of the parts for their inspection during serial-line production, especially in the automotive industry. A methodology of the sensor planning system consists of positions planning, their simulation for true visibility of the part elements using a reflectance model, and a simulation of the positions for robot reachability. Compared to previous studies, visual properties of the scanned parts’ surface can be simulated precisely. The Nayar model is used as a reflectance model. This model is suitable for materials that are characterized by the combination of diffuse and specular reflections and uses three components of reflection: diffuse, specular lobe and specular spike. Results of the scanning that were obtained using an ATOS III Triple Scan fringe projection 3D scanner and a KUKA KR 60 HA industrial robot were compared to the simulation. The comparison based on the correspondence of the polygons area acquired in each sensor position (in simulation and in scanning) shows that in the performed measurements the median of differences between simulation and scanning is around 16%.

Paper Details

Date Published: 22 June 2015
PDF: 13 pages
Proc. SPIE 9530, Automated Visual Inspection and Machine Vision, 953005 (22 June 2015); doi: 10.1117/12.2184890
Show Author Affiliations
Tomáš Koutecký, Brno Univ. of Technology (Czech Republic)
David Paloušek, Brno Univ. of Technology (Czech Republic)
Jan Brandejs, Brno Univ. of Technology (Czech Republic)

Published in SPIE Proceedings Vol. 9530:
Automated Visual Inspection and Machine Vision
Jürgen Beyerer; Fernando Puente León, Editor(s)

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