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Proceedings Paper

3D model matching for fine pose determination
Author(s): Thomas Peurach; Margaret J. Whalen; Douglas Haanpaa
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Paper Abstract

3D model matching is the process of matching image features to 3D model features. The feature correspondences are used to determine the transformation between the model and camera and thus, define the orientation of the object. The solution to this 3D model matching is difficult to calculate due to the high dimensionality of the solution space. Addressing this problem, we have developed a machine-vision system which incorporates an advanced iterative search procedure and Hough transform feature extraction into a system which avoids image segmentation to determine the fine pose of some objects. Inputs to the system include images of the object from various camera views, a coarse pose estimate and a CAD object model. A windowing grid system is applied to the scene image(s) and a single feature hypothesis is extracted from each window. A modified Newton-Raphson iterative search method is used to evaluate mappings between the image features and model features. The search attempts to optimize a cost function which is based on the perpendicular distance between linear features. Results have shown that the described fine pose determination method can achieve sub pixel accuracy when applied to images of various geometric shapes. The system has been applied to a number of applications including space rendezvous and docking, ordnance defusing, and missile tracking.

Paper Details

Date Published: 3 October 1995
PDF: 9 pages
Proc. SPIE 2588, Intelligent Robots and Computer Vision XIV: Algorithms, Techniques, Active Vision, and Materials Handling, (3 October 1995); doi: 10.1117/12.222700
Show Author Affiliations
Thomas Peurach, Cybernet Systems Corp. (United States)
Margaret J. Whalen, Cybernet Systems Corp. (United States)
Douglas Haanpaa, Cybernet Systems Corp. (United States)

Published in SPIE Proceedings Vol. 2588:
Intelligent Robots and Computer Vision XIV: Algorithms, Techniques, Active Vision, and Materials Handling
David P. Casasent, Editor(s)

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