Clustering in image space for place recognition and visual annotations for human-robot interaction

The most classical way of attempting to solve the vision-guided navigation problem for autonomous robots corresponds to the use of three-dimensional (3-D) geometrical descriptions of the scene; what is known as model-based approaches. However, these approaches do not facilitate the user's task...

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Bibliographic Details
Published in:IEEE transactions on cybernetics 2001-10, Vol.31 (5), p.669-682
Main Authors: Martinez, A.M., Vitria, J.
Format: Article
Language:English
Subjects:
Algorithms
Annotations
Construction
Discriminant analysis
Image recognition
Layout
Linear discriminant analysis
Mathematical models
Mobile robots
Motion planning
Navigation
Orbital robotics
Pattern recognition
Principal component analysis
Principal components analysis
Robot vision systems
Robots
Solid modeling
Studies
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Summary:The most classical way of attempting to solve the vision-guided navigation problem for autonomous robots corresponds to the use of three-dimensional (3-D) geometrical descriptions of the scene; what is known as model-based approaches. However, these approaches do not facilitate the user's task because they require that geometrically precise models of the 3-D environment be given by the user. In this paper, we propose the use of "annotations" posted on some type of blackboard or "descriptive" map to facilitate this user-robot interaction. We show that, by using this technique, user commands can be as simple as "go to label 5." To build such a mechanism, new approaches for vision-guided mobile robot navigation have to be found. We show that this can be achieved by using mixture models within an appearance-based paradigm. Mixture models are more useful in practice than other pattern recognition methods such as principal component analysis (PCA) or Fisher discriminant analysis (FDA)-also known as linear discriminant analysis (LDA), because they can represent nonlinear subspaces. However, given the fact that mixture models are usually learned using the expectation-maximization (EM) algorithm which is a gradient ascent technique, the system cannot always converge to a desired final solution, due to the local maxima problem. To resolve this, a genetic version of the EM algorithm is used. We then show the capabilities of this latest approach on a navigation task that uses the above described "annotations.".
ISSN:1083-4419
2168-2267
1941-0492
2168-2275
DOI:10.1109/3477.956029