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Proceedings Paper

3D object recognition using deformable model for negating sensing error
Author(s): Nobutaka Kimura; Toshio Moriya
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Paper Abstract

Focusing on 3D object recognition for handling-robot tasks, we developed a registration method for point data measured from a real object and model surfaces. On the basis of the iterative-closest-point (ICP) algorithm, we proposed a registration technique that deforms model shapes instead of correcting measured range data including distance errors. We call our technique a "viewpoint-dependent remodeling ICP" algorithm. Even when a laser range finder only is used, this technique can reduce the effects of errors depending on surface characteristics such as colors and reflectance properties. In the preliminary stages, the relationships between distance errors and surface characteristics of points on object surfaces are determined and added to the models. In object recognition stages, we measure point data, and do registration while changing the model position and attitude and deforming the model shape. The deformation depends on the relationships and the relative positions of the model surfaces and the sensor position. In preliminary experimental tests, we measured distances to black and white papers and evaluated the distance errors. Moreover, we simulated recognizing the bottle covered with these papers. In this simulation, it was verified that our technique has convergence and improves accuracy of correspondence estimations between measured data and models.

Paper Details

Date Published: 3 February 2009
PDF: 8 pages
Proc. SPIE 7251, Image Processing: Machine Vision Applications II, 72510K (3 February 2009); doi: 10.1117/12.804913
Show Author Affiliations
Nobutaka Kimura, Hitachi, Ltd. (Japan)
Toshio Moriya, Hitachi, Ltd. (Japan)


Published in SPIE Proceedings Vol. 7251:
Image Processing: Machine Vision Applications II
Kurt S. Niel; David Fofi, Editor(s)

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