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ICRA
2006
IEEE

Probabilistic Location Recognition using Reduced Feature Set

13 years 9 months ago
Probabilistic Location Recognition using Reduced Feature Set
Abstract— 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 bet...
Fayin Li, Jana Kosecka
Added 11 Jun 2010
Updated 11 Jun 2010
Type Conference
Year 2006
Where ICRA
Authors Fayin Li, Jana Kosecka
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