Bayesian Landmark Learning for Mobile Robot Localization

Sebastian Thrun

To operate successfully in indoor environments, mobile robots must be able to localize themselves. Most current localization algorithms lack flexibility, autonomy, and often optimality, since they rely on a human to determine what aspects of the sensor data to use in localization (\eg what landmarks to use). This paper describes a learning algorithm, called BaLL, that enables mobile robots to learn what features/landmarks are best suited for localization, and also to train artificial neural networks for extracting them from the sensor data. A rigorous Bayesian analysis of probabilistic localization is presented, which produces a rational argument for evaluating features, for selecting them optimally, and for training the networks that approximate the optimal solution. In a systematic experimental study, BaLL is found to outperform two other recent approaches to mobile robot localization.

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@ARTICLE{Thrun98b,
  AUTHOR         = {S. Thrun},
  YEAR           = {1998},
  TITLE          = {{B}ayesian Landmark Learning for Mobile Robot 
                    Localization},
  JOURNAL        = {Machine Learning},
  VOLUME         = {33},
  NUMBER         = {1},
  PAGES          = {41--76}
}