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Probabilistic location recognition using reduced feature set

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2 Author(s)
Fayin Li ; Dept. of Comput. Sci., George Mason Univ., Fairfax, VA ; Kosecka, J.

The localization capability is central to basic navigation tasks and motivates development of various visual navigation systems. In this paper we describe a two stage approach for localization in indoor environments. In the first stage, the environment is partitioned into several locations, each characterized by a set of scale-invariant keypoints and their associated descriptors. In the second stage the keypoints of the query view are integrated probabilistically yielding an estimate of most likely location. The novelty of our approach is in the selection of discriminative features, best suited for characterizing individual locations. We demonstrate that high location recognition rate is maintained with only 10% of the originally detected features, yielding a substantial speedup in recognition and capability of handling larger environments. The ambiguities due to the self-similarity and dynamic changes in the environment are resolved by exploiting spatial relationships between locations captured by hidden Markov model

Published in:

Robotics and Automation, 2006. ICRA 2006. Proceedings 2006 IEEE International Conference on

Date of Conference:

15-19 May 2006