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Qualitative image based localization in indoors environments

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4 Author(s)
Kosecka, J. ; Dept. of Comput. Sci., George Mason Univ., Fairfax, VA, USA ; Liang Zhou ; Barber, P. ; Duric, Z.

Man made indoor environments possess regularities, which can be efficiently exploited in automated model acquisition by means of visual sensing. In this context we propose an approach for inferring a topological model of an environment from images or the video stream captured by a mobile robot during exploration. The proposed model consists of a set of locations and neighborhood relationships between them. Initially each location in the model is represented by a collection of similar, temporally adjacent views, with the similarity defined according to a simple appearance based distance measure. The sparser representation is obtained in a subsequent learning stage by means of learning vector quantization (LVQ). The quality of the model is tested in the context of qualitative localization scheme by means of location recognition: given a new view, the most likely location where that view came from is determined.

Published in:

Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on  (Volume:2 )

Date of Conference:

18-20 June 2003