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Proceedings Paper

Determining the Pose of an Object
Author(s): R. M. Dolezal; T. N. Mudge; J. L. Turney; R. A. Volz
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Paper Abstract

We present an algorithm for determining the position and orientation (pose) of an unoccluded three-dimensional object given a digitized grey-scale image. A model data base of characteristic views is generated prior to run-time by merging perspective views containing the same feature points, such as points of sharp curvature in an edge map, into common characteristic views. The run-time algorithm consists of (1) extracting an edge map from the image; (2) locating feature points in the edge map; (3) using intrinsic properties of the feature points in the image, such as signs of curvature, to rank the characteristic views for the object according to their likelihood of correspondence to the image; (4) for each characteristic view in the ranking, matching properties of the image feature points and object feature points in order to generate potential correspondences; and (5) verifying the most likely correspondences by examining a least-squares fit in each correspondence. The fit yields a rotation matrix that defines the pose of the object.

Paper Details

Date Published: 9 June 1986
PDF: 4 pages
Proc. SPIE 0595, Computer Vision for Robots, (9 June 1986); doi: 10.1117/12.952245
Show Author Affiliations
R. M. Dolezal, University of Michigan (United States)
T. N. Mudge, University of Michigan (United States)
J. L. Turney, University of Michigan (United States)
R. A. Volz, University of Michigan (United States)

Published in SPIE Proceedings Vol. 0595:
Computer Vision for Robots
Olivier D. Faugeras; Robert B. Kelley, Editor(s)

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