Simultaneous Localization and Mapping with Unknown Data Association Using FastSLAM

Michael Montemerlo and Sebastian Thrun

The Extended Kalman Filter (EKF) has been the de facto approach to the Simultaneous Localization and Mapping (SLAM) problem for nearly fifteen years. However, the EKF has two serious deficiencies that prevent it from being applied to large, real-word environments: quadratic complexity and sensitivity to failures in data association. FastSLAM, an alternative approach based on the Rao-Blackwellized Particle Filter, has been shown to scale logarithmically with the number of landmarks in the map [ref]. This efficiency enables FastSLAM to be applied to environments far larger than could be handled by the EKF. In this paper, we will show that FastSLAM also substantially outperforms the EKF in environments with ambiguous data association. The performance of the two algorithms is compared on a real-world data set with various levels of odometric noise. In addition, we will show how negative information can be incorporated into FastSLAM in order to improve the accuracy of the estimated map.

The full paper is available in gzipped Postscript and PDF

@UNPUBLISHED{Montemerlo02d,
  AUTHOR         = {M. Montemerlo and S. Thrun},
  TITLE          = {Simultaneous Localization and Mapping with Unknown 
                    Data Association Using {FastSLAM}},
  YEAR           = {2002},
  ORGANIZATION   = {Carnegie Mellon University},
  ADDRESS        = {Pittsburgh, PA},
  NOTE           = {Submitted for publication}
}