NDLI: Omniview-based concurrent map building and localization using adaptive appearance maps

Content Provider	IEEE Xplore Digital Library
Author	Gross, H.-M. Koenig, A. Mueller, S.
Copyright Year	2005
Description	Author affiliation: Dept. of Neuroinformatics & Cognitive Robotics, Ilmenau Tech. Univ., Germany (Gross, H.-M.; Koenig, A.; Mueller, S.)
Abstract	This paper describes a novel omnivision-based concurrent map-building and localization (CML) approach which is able to robustly localize a mobile robot in a uniformly structured, maze-like environment with changing appearances. The presented approach extends and improves known appearance-based CML techniques in a few essential aspects. For example, an advanced learning scheme in combination with an active forgetting is introduced to allow a complexity restricting adaptation of the environment model to appearance variations of the operation area. Moreover, a generalized scheme for fusion of localization hypotheses from several state estimators with different meaning and certainty and a distributed coding of the current observation by a weighted set of reference observations is proposed. Finally, several real-world localization experiments investigating the stability and localization accuracy of this novel omnivision-based CML technique for a highly dynamic and populated operation area, a home store, are presented.
Starting Page	3510
Ending Page	3515
File Size	1578292
Page Count	6
File Format	PDF
ISBN	0780392981
DOI	10.1109/ICSMC.2005.1571691
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-10-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robustness Mobile robots Robot sensing systems Cognitive robotics State estimation Stability History Feature extraction Human robot interaction Robot vision systems
Content Type	Text
Resource Type	Article

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