Collective Classification for Labeling of Places and Objects in 2D and 3D Range Data

  • Rudolph Triebel
  • Óscar Martínez Mozos
  • Wolfram Burgard
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


In this paper, we present an algorithm to identify types of places and objects from 2D and 3D laser range data obtained in indoor environments. Our approach is a combination of a collective classification method based on associative Markov networks together with an instance-based feature extraction using nearest neighbor. Additionally, we show how to select the best features needed to represent the objects and places, reducing the time needed for the learning and inference steps while maintaining high classification rates. Experimental results in real data demonstrate the effectiveness of our approach in indoor environments.


Feature Vector Indoor Environment Collective Classification IEEE Computer Vision Unlabeled Data Point 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Rudolph Triebel
    • 1
  • Óscar Martínez Mozos
    • 2
  • Wolfram Burgard
    • 2
  1. 1.Autonomous Systems LabETH ZürichSwitzerland
  2. 2.Department of Computer ScienceUniversity of FreiburgGermany

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