Entropy-based features for robust place recognition
Rady, Sherine; Wagner, Achim; Badreddin, Essam;
Abstract
In this paper, an appearance-based modeling of the environment is presented for the sake of mobile robot localization. The model allows perception and recognition within a topological context. Highly descriptive SIFT is used to extract local features from visual data acquired from an indoor environment. A method is developed to select those features, which are best for localization using a probabilistic modeling and an entropy measure. The impact of feature selection on the localization performance is more than 60% reduction in the storage and recognition time overhead. The methodology insures the recognition of different places with 96% precision, in spite of perceptual aliasing and image variability. © 2008 IEEE.
Other data
Title | Entropy-based features for robust place recognition | Authors | Rady, Sherine ; Wagner, Achim; Badreddin, Essam | Keywords | Clustering;Place recognition;Topological localization;SIFT;Feature reduction;Environment modeling;Entropy | Issue Date | 1-Dec-2008 | Conference | Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics | ISSN | 1062922X | DOI | 10.1109/ICSMC.2008.4811362 | Scopus ID | 2-s2.0-69949185516 |
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