NDLI: Supervised Learning of Topological Maps using Semantic Information Extracted from Range Data

Content Provider	IEEE Xplore Digital Library
Author	Mozos, O.M. Burgard, W.
Copyright Year	2006
Description	Author affiliation: Dept. of Comput. Sci., Freiburg Univ. (Mozos, O.M.; Burgard, W.)
Abstract	This paper presents an approach to create topological maps from geometric maps obtained with a mobile robot in an indoor-environment using range data. Our approach utilizes AdaBoost, a supervised learning algorithm, to classify each point of the geometric map into semantic classes. We then apply a segmentation procedure based on probabilistic relaxation labeling on the resulting classifications to eliminate errors. The topological graph is then extracted from the individual different regions and their connections. In this way, we obtain a topological map in the form of a graph, in which each node indicates a region in the environment with its corresponding semantic class (e.g., corridor, or room) and the edges indicate the connections between them. Experimental results obtained with data from different real-world environments demonstrate the effectiveness of our approach
Starting Page	2772
Ending Page	2777
File Size	415403
Page Count	6
File Format	PDF
ISBN	1424402581
DOI	10.1109/IROS.2006.282058
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-10-09
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Supervised learning Data mining Mobile robots Labeling Indoor environments Solid modeling Intelligent robots Computer science Cognitive robotics Buildings
Content Type	Text
Resource Type	Article

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