Indoor environments can typically be divided into places with
different functionalities like corridors, kitchens, offices, or
seminar rooms. The ability to learn such semantic
categories from sensor data enables a mobile robot to
extend the representation of the environment facilitating the interaction with humans.
As an example, natural language terms like ``corridor" or ``room" can be used to communicate the
position of the robot in a map in a more intuitive way. 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 range data and vision
into a strong classifier. We present two main applications of this approach. Firstly, we show how
our approach can be utilized by a moving robot for an online
classification of the poses traversed along its path using a hidden
Markov model. Secondly, we introduce an approach to learn
topological maps from geometric maps by applying our semantic
classification procedure in combination with a probabilistic relaxation procedure. We finally show how to apply associative Markov networks (AMNs) together with AdaBoost for classifying complete geometric maps. Experimental results obtained in simulation and with real robots
demonstrate the effectiveness of our approach in various indoor environments.
Paper: [pdf: 1.9M]
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}
}