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Proceedings Paper

Three-dimensional object recognition using multiple sensors
Author(s): Jay K. Hackett; Matt J. Lavoie; Mubarak Ali Shah
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Paper Abstract

Multi-sensor fusion deals with the combination of complementary and sometimes contradictory sensor data into a reliable estimate of the environment to achieve a sum which is better than the parts. Multiple sensors can be used to overcome problems associated with object recognition systems. The introduction of multiple sensors into such a system emphasizes the need for useful methods for combining sensor outputs. Multiple sensors can yield duplicate information that can be used to verify input and possibly to ease the task of object recognition. Since each sensor output contains noise, multiple sensors can be used to determine the same property, but with the consensus of all sensors. We introduce a Bayesian approach for combining sensor outputs that increases the confidence in features supported by multiple sensors and reduces the confidence in unsupported features. This paper describes how feature level input from an arbitrary number of sensors may be combined to make 3-D object recognition more accurate. An example involving features from range, intensity, and tactile is given.

Paper Details

Date Published: 1 April 1991
PDF: 12 pages
Proc. SPIE 1383, Sensor Fusion III: 3D Perception and Recognition, (1 April 1991); doi: 10.1117/12.25301
Show Author Affiliations
Jay K. Hackett, Univ. of Central Florida (United States)
Matt J. Lavoie, Univ. of Central Florida (United States)
Mubarak Ali Shah, Univ. of Central Florida (United States)

Published in SPIE Proceedings Vol. 1383:
Sensor Fusion III: 3D Perception and Recognition
Paul S. Schenker, Editor(s)

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