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Multi-view object instance recognition in an industrial context

Published online by Cambridge University Press:  23 June 2015

Wail Mustafa Open the ORCID record for Wail Mustafa [Opens in a new window] ,
Nicolas Pugeault ,
Anders G. Buch  and
Norbert Krüger
Wail Mustafa*
Affiliation:
Mærsk Mc-Kinney Møller Institute, University of Southern Denmark, Campusvej 55, DK-5230 Odense M, Denmark. Email: [email protected]
Nicolas Pugeault
Affiliation:
Centre for Vision, Speech and Signal Processing, Faculty of Engineering & Physical Sciences, University of Surrey, Guildford GU2 7XH, UK
Anders G. Buch
Affiliation:
Mærsk Mc-Kinney Møller Institute, University of Southern Denmark, Campusvej 55, DK-5230 Odense M, Denmark. Email: [email protected]
Norbert Krüger
Affiliation:
Mærsk Mc-Kinney Møller Institute, University of Southern Denmark, Campusvej 55, DK-5230 Odense M, Denmark. Email: [email protected]
*
*Corresponding author. Email: [email protected]
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Summary

We present a fast object recognition system coding shape by viewpoint invariant geometric relations and appearance information. In our advanced industrial work-cell, the system can observe the work space of the robot by three pairs of Kinect and stereo cameras allowing for reliable and complete object information. From these sensors, we derive global viewpoint invariant shape features and robust color features making use of color normalization techniques.

We show that in such a set-up, our system can achieve high performance already with a very low number of training samples, which is crucial for user acceptance and that the use of multiple views is crucial for performance. This indicates that our approach can be used in controlled but realistic industrial contexts that require—besides high reliability—fast processing and an intuitive and easy use at the end-user side.

Type
Articles
Information
Robotica , Volume 35 , Issue 2 , February 2017 , pp. 271 - 292
Copyright
Copyright © Cambridge University Press 2015 

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