A highly efficient FastSLAM algorithm for generating cyclic maps of large-scale environments from raw laser range measurements.

D. Haehnel, D. Fox, W. Burgard, and S. Thrun.

The ability to learn a consistent model of its environment is a prerequisite for autonomous mobile robots. A particularly challenging problem in acquiring environment maps is that of closing loops; loops in the environment create challenging data association problems [9]. This paper presents a novel algorithm that combines RaoBlackwellized particle filtering and scan matching. In our approach scan matching is used for minimizing odometric errors during mapping. A probabilistic model of the residual errors of scan matching process is then used for the resampling steps. This way the number of samples required is seriously reduced. Simultaneously we reduce the particle depletion problem that typically prevents the robot from closing large loops. We present extensive experiments that illustrate the superior performance of our approach compared to previous approaches.

The full paper is available in PDF and gzipped Postscript

@INPROCEEDINGS{Haehnel03b,
  AUTHOR	= {H\"{a}hnel, D. and Fox, D. and Burgard, W. and Thrun, S.},
  TITLE		= {A Highly Efficient {FastSLAM} Algorithm for Generating Cyclic Maps of Large-Scale Environments from Raw Laser Range Measurements},
  YEAR		= {2003},
  BOOKTITLE	= {Proceedings of the Conference on Intelligent Robots and Systems (IROS)}
}