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Proceedings Paper

Integrated vision system for object identification and localization using 3-D geometrical models
Author(s): Clint R. Bidlack; Mohan M. Trivedi
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Paper Abstract

Successful implementation of sensor based robots in dynamic environments will depend largely upon the immunity of the system to incomplete and erroneous sensory information. This paper introduces a six module, 3D model based robot vision system, which utilizes 3D geometric models of the objects expected to appear in a scene and can tolerate incomplete and noisy image features. Object identification is independent of the particular robot pose and object pose, as long as the object is within view of the camera. The system effectively utilizes topology during the object identification phase to reduce the number of mappings between the domain of image features to that of the object features (object models). Geometric information is then employed by the Pose Determination Module to decipher the identified objects unconstrained position and orientation. Continuing experimentation is giving valuable insight into the characteristics of our strategy and has verified the system performance when using incomplete image feature sets.

Paper Details

Date Published: 1 March 1991
PDF: 11 pages
Proc. SPIE 1468, Applications of Artificial Intelligence IX, (1 March 1991); doi: 10.1117/12.45471
Show Author Affiliations
Clint R. Bidlack, Univ. of Tennessee/Knoxville (United States)
Mohan M. Trivedi, Univ. of Tennessee/Knoxville (United States)

Published in SPIE Proceedings Vol. 1468:
Applications of Artificial Intelligence IX
Mohan M. Trivedi, Editor(s)

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