3D Environment Modeling Based on Surface Primitives

  • Michael Ruhnke
  • Bastian Steder
  • Giorgio Grisetti
  • Wolfram Burgard
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 76)


In this article we describe an algorithm for constructing a compact representation of 3D laser range data. Our approach extracts a dictionary of local scans from the scene. The words of this dictionary are used to replace recurrent local 3D structures, which leads to a substantial compression of the entire point cloud. We optimize our model in terms of complexity and accuracy by minimizing the Bayesian information criterion (BIC). Experimental evaluations on large real-world datasets show that the described method allows robots to accurately reconstruct environments with as few as 70 words. Furthermore the experiments suggest that the proposed representation gives a richer semantic description than pure occupancy based representations.


Point Cloud Object Detection Dictionary Size Dictionary Word Reconstructed Scene 
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 GmbH Berlin Heidelberg 2012

Authors and Affiliations

  • Michael Ruhnke
    • 1
  • Bastian Steder
    • 1
  • Giorgio Grisetti
    • 2
  • Wolfram Burgard
    • 1
  1. 1.Institut für InformatikAlbert-Ludwigs-Universität FreiburgFreiburgGermany
  2. 2.Dept. of Systems and Computer ScienceLa Sapienza University of RomeRomeItaly

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