Abstract
Indoor environments can typically be divided into places with
different functionalities like corridors, rooms or doorways. 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 sensor range data 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. In this case we additionally use as features objects extracted from images. Secondly, we introduce an approach to learn
topological maps from geometric maps by applying our semantic
classification procedure in combination with a probabilistic relaxation method.
Alternatively, we apply associative Markov networks to classify geometric maps and compare the results with the relaxation approach.
Experimental results obtained in simulation and with real robots
demonstrate the effectiveness of our approach in various indoor environments.
Paper: [pdf]
Bibtex
@article{mozos2007ras,
title = {Supervised semantic labeling of places using information extracted from sensor data},
author = {Oscar Martinez Mozos and Rudolph Triebel and Patric Jensfelt and Axel Rottmann and Wolfram Burgard},
journal = {Robotics and Autonomous Systems},
volume = {55},
number = {5},
year = {2007},
month = {May},
pages = {391--402},
url = {http://www.informatik.uni-freiburg.de/~omartine/publications/mozos2007RAS.pdf}
}
Journal Information
Robotics and Autonomous Systems.
Volume 55, issue 5, pp 391-402. May, 2007.
Journal Citation Report Impact Factor (2006): 0.832
Cites (2005): 536
Subject Category: AUTOMATION & CONTROL SYSTEMS / COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE / ROBOTICS
Position in the category (2005): 23 of 51 / 49 of 85 / 4 of 12