Indoor environments can typically be divided into places with different functionalities
like corridors, kitchens, offices, or seminar rooms. We believe that the ability to learn
such semantic categories from sensor data or in maps enables a mobile robot to more efficiently
accomplish a variety of tasks such as human-robot interaction, path-planning, exploration,
or localization. In this work, we first propose an approach based on supervised learning
to classify the pose of a mobile robot into semantic classes. Our method uses AdaBoost to
boost simple features extracted from vision and laser range data into a strong classifier. We
furthermore present two main applications of this approach. Firstly, we show how our approach
can be utilized by a moving robot for robust online classification of the poses traversed
along its path using a hidden Markov model. Secondly, we introduce a new approach to learn
topological maps from geometric maps by applying our semantic classification procedure in
combination with probabilistic labeling. Experimental results obtained in simulation and with
real robots demonstrate the effectiveness of our approach in various environments.
Draft: [pdf: 745k]
Online: Springer Tracts in Advanced Robotics
Bibtex
@InBook{mozos2007star,
title = {Robotics Research: Results of the 12th International Symposium ISRR.},
author = {Oscar Martinez Mozos and Cyrill Stachniss and Axel Rottmann and Wolfram Burgard},
chapter = {Using AdaBoost for Place Labeling and Topological Map Building.},
year = {2007},
pages = {453--472},
editor = {Thrun, S. and Brooks, R. and Durrant-Whyte, H.},
publisher = {Springer-Verlag Berlin Heidelberg, Germany},
series = {{STAR} Springer tracts in advanced robotics},
volume = {28}
}