FastSLAM: A Factored Solution to the Simultaneous Localization and Mapping Problem

Michael Montemerlo, Sebastian Thrun, Daphne Koller, Ben Wegbreit

The ability to simultaneously localize a robot and accurately map its surroundings is considered by many to be a key prerequisite of truly autonomous robots. However, few approaches to this problem scale up to handle the very large number of landmarks present in real environments. Kalman filter-based algorithms, for example, require time quadratic in the number of landmarks to incorporate each sensor observation. This paper presents FastSLAM, an algorithm that recursively estimates the full posterior distribution over robot pose and landmark locations, yet scales logarithmically with the number of landmarks in the map. This algorithm is based on a factorization of the posterior into a product of conditional landmark distributions and a distribution over robot paths. The algorithm has been run successfully on as many as 50,000 landmarks, environments far beyond the reach of previous approaches. Experimental results demonstrate the advantages and limitations of the FastSLAM algorithm on both simulated and real-world data.

The full paper is available in gzipped Postscript and PDF

@INPROCEEDINGS{Montemerlo02a,
  AUTHOR         = {Montemerlo, M. and Thrun, S. and Koller, D. and 
                    Wegbreit, B.},
  TITLE          = {{FastSLAM}: {A} Factored Solution to the Simultaneous 
                    Localization and Mapping Problem},
  YEAR           = {2002},
  BOOKTITLE      = {Proceedings of the AAAI National Conference on 
                    Artificial Intelligence},
  PUBLISHER      = {AAAI},
  ADDRESS        = {Edmonton, Canada}
}