Supervised Semantic Labeling of Places using Information Extracted from Laser and Vision Sensor Data

Oscar Martinez Mozos, Rudolph Triebel, Patric Jensfelt, Axel Rottmann and Wolfram Burgard

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.

BibTeX Entry:

@Article{MartinezMozos07a,
  author =       {Oscar Mart\'{i}nez Mozos and Rudolph Triebel and Patric Jensfelt and Axel Rottmann and Wolfram Burgard},
  title =        {Supervised Semantic Labeling of Places using Information Extracted from Laser and Vision Sensor Data},
  journal =      {Robotics and Autonomous Systems Journal},
  year =         2007,
  volume =       55,
  number =       5,
  pages =        {391--402},
  month =        may
}

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