DocumentCode :
3180724
Title :
Supervised Learning of Topological Maps using Semantic Information Extracted from Range Data
Author :
Mozos, Oscar Martinez ; Burgard, Wolfram
Author_Institution :
Dept. of Comput. Sci., Freiburg Univ.
fYear :
2006
fDate :
9-15 Oct. 2006
Firstpage :
2772
Lastpage :
2777
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
Keywords :
graph theory; information retrieval; learning (artificial intelligence); mobile robots; path planning; pattern classification; AdaBoost; geometric maps; mobile robot; range data; semantic information extraction; supervised learning; topological graph; topological maps; Buildings; Cognitive robotics; Computer science; Data mining; Indoor environments; Intelligent robots; Labeling; Mobile robots; Solid modeling; Supervised learning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Robots and Systems, 2006 IEEE/RSJ International Conference on
Conference_Location :
Beijing
Print_ISBN :
1-4244-0258-1
Electronic_ISBN :
1-4244-0259-X
Type :
conf
DOI :
10.1109/IROS.2006.282058
Filename :
4058812
Link To Document :
بازگشت