D. Sack and W. Burgard

A comparison of methods for line extraction from range data

In Proc. of the 5th IFAC Symposium on Intelligent Autonomous Vehicles (IAV), 2004.






Abstract:

The representation of the environment of a mobile robot by line models is a popular alternative to occupancy grid maps. Line maps require significantly less memory than occupancy grids and therefore scale better with the size of the environment. They furthermore are more accurate since they do not suffer from discretization problems. In the past a variety of techniques for learning line maps from range data have been developed. These techniques differ in various aspects such as the way lines are extracted from range scans, how the lines are updated upon sensory input. There furthermore are techniques that are able to operate online, whereas others postprocess the data. In this paper we compare three different techniques for learning line models with respect to various parameters such as efficiency and quality of the resulting maps. Experimental results illustrate the advantages and the disadvantages of the different techniques.


Bibtex:

@InProceedings{sack04lines,
  author    = {Sack, D. and Burgard, W.},
  title     = {A comparison of methods for line extraction from range data},
  booktitle = {Proc.~of the 5th IFAC Symposium on Intelligent Autonomous Vehicles (IAV)},
  year      = {2003}
}



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