Results for Outdoor-SLAM Using Sparse Extended Information Filters

Yufeng Liu and Sebastian Thrun

In [Thrun et al, 02], a new algorithm was proposed for efficiently solving the simultaneous localization and mapping (SLAM) problem. In this paper, we extend this algorithm to handle data association problems and report real-world results, obtained with an outdoor vehicle. We find that our approach performs favorably when compared to the extended Kalman filter solution from which it is derived.

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@UNPUBLISHED{Liu02a,
  AUTHOR         = {Y. Liu and S. Thrun},
  TITLE          = {Results for Outdoor-{SLAM} using Sparse Extended 
                    Information Filters},
  YEAR           = {2002},
  ORGANIZATION   = {Carnegie Mellon University},
  ADDRESS        = {Pittsburgh, PA},
  NOTE           = {Submitted for publication}
}