Óscar Martínez Mozos and Wolfram Burgard.
Supervised Learning of Topological Maps using Semantic Information Extracted from Range Data.
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
pp. 2772-2777. ISBN: 1-4244-0259-X
Beijing, China, 2006.

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 uses 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 classications to eliminate errors. The topological graph is then extracted from the individual dierent 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 dierent real-world environments demonstrate the effectiveness of our approach.

Paper: [pdf: 381k]

Bibtex

@InProceedings{mozos2006iros_w,
  TITLE     = {Semantic Labeling of Places using Information Extracted from Laser and Vision Sensor Data},
  AUTHOR    = {Oscar Martinez Mozos and Axel Rottmann and Rudolph Triebel and Patric Jensfelt and Wolfram Burgard},
  BOOKTITLE = {In Proceedings of the IEEE/RSJ IROS Workshop: From sensors to human spatial concepts},
  ADDRESS   = {Beijing, China},
  YEAR      = {2006},
  URL 		= {http://www.informatik.uni-freiburg.de/~omartine/publications/mozos2006iros_w.pdf}
}