M. Ruhnke, B. Steder, G. Grisetti, and W. Burgard.
3D Environment Modeling Based on Surface Primitives.
Towards Service Robots for Everyday Environments, :281-300, 2012.

Abstract

In this chapter 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 en- vironments with as few as 70 words. Furthermore the experiments suggest that the proposed representation gives a richer semantic description than pure occupancy based representations.

BibTeX entry:

@article{ruhnke12desire,
  author = {Ruhnke, M. and Steder, B. and Grisetti, G. and Burgard, W.},
  publisher = {Springer Berlin/Heidelberg},
  title = {{3D} Environment Modeling Based on Surface Primitives},
  journal = {Towards Service Robots for Everyday Environments},
  year = {2012},
  pages = {281--300}
}