Fastslam: An efficient solution to the simultaneous localization and mapping problem with unknown data association.

S. Thrun, M. Montemerlo, D. Koller, B. Wegbreit, J. Nieto, and E. Nebot.

This article provides a comprehensive description of FastSLAM, a new family of algorithms for the simultaneous localization and mapping problem, which specifically address hard data association problems. The algorithm uses a particle filter for sampling robot paths, and extended Kalman filters for representing maps acquired by the vehicle. This article presents two variants of this algorithm, the original algorithm along with a more recent variant that provides improved performance in certain operating regimes. In addition to a mathematical derivation of the new algorithm, we present a proof of convergence and experimental results on its performance on real-world data.

The full paper is available in PDF and gzipped Postscript

@ARTICLE{Thrun03g,
  AUTHOR	= {S. Thrun and M. Montemerlo and D. Koller and B. Wegbreit and J. Nieto and E. Nebot},
  TITLE		= {FastSLAM: An Efficient Solution to the Simultaneous Localization And Mapping Problem with Unknown Data Association},
  YEAR		= {2004},
  JOURNAL       = {Journal of Machine Learning Research},
  NOTE          = {To appear}
}