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Proceedings Paper

A comparative study of multi-sensor data fusion methods for highly accurate assessment of manufactured parts
Author(s): Ammar Hannachi; Sophie Kohler; Alex Lallement; Ernest Hirsch
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Paper Abstract

3D modeling of scene contents takes an increasing importance for many computer vision based applications. In particular, industrial applications of computer vision require efficient tools for the computation of this 3D information. Routinely, stereo-vision is a powerful technique to obtain the 3D outline of imaged objects from the corresponding 2D images. As a consequence, this approach provides only a poor and partial description of the scene contents. On another hand, for structured light based reconstruction techniques, 3D surfaces of imaged objects can often be computed with high accuracy. However, the resulting active range data in this case lacks to provide data enabling to characterize the object edges. Thus, in order to benefit from the positive points of various acquisition techniques, we introduce in this paper promising approaches, enabling to compute complete 3D reconstruction based on the cooperation of two complementary acquisition and processing techniques, in our case stereoscopic and structured light based methods, providing two 3D data sets describing respectively the outlines and surfaces of the imaged objects. We present, accordingly, the principles of three fusion techniques and their comparison based on evaluation criterions related to the nature of the workpiece and also the type of the tackled application. The proposed fusion methods are relying on geometric characteristics of the workpiece, which favour the quality of the registration. Further, the results obtained demonstrate that the developed approaches are well adapted for 3D modeling of manufactured parts including free-form surfaces and, consequently quality control applications using these 3D reconstructions.

Paper Details

Date Published: 30 April 2015
PDF: 8 pages
Proc. SPIE 9534, Twelfth International Conference on Quality Control by Artificial Vision 2015, 95340X (30 April 2015); doi: 10.1117/12.2182832
Show Author Affiliations
Ammar Hannachi, ICUBE, CNRS, Univ. Strasbourg (France)
Sophie Kohler, Lab. MIPS, Univ. de Haute Alsace (France)
Alex Lallement, ICUBE, CNRS, Univ. Strasbourg (France)
Ernest Hirsch, ICUBE, CNRS, Univ. Strasbourg (France)

Published in SPIE Proceedings Vol. 9534:
Twelfth International Conference on Quality Control by Artificial Vision 2015
Fabrice Meriaudeau; Olivier Aubreton, Editor(s)

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