@InProceedings{plagemann07rss,
  TITLE     = {Gaussian Beam Processes: A Nonparametric Bayesian Measurement Model for Range Finders},
  AUTHOR    = {Plagemann, C. and Kersting, K. and Pfaff, P. and Burgard, W.},
  BOOKTITLE = {Robotics: Science and Systems (RSS)},
  ADDRESS   = {Atlanta, Georgia, USA},
  YEAR      = {2007},
  MONTH     = {June},
  PDFURL    = {http://www.informatik.uni-freiburg.de/~plagem/bib/plagemann07rss.pdf},
  ABSTRACT  = {In probabilistic mobile robotics, the development of measurement models plays a crucial role as it directly influences the efficiency and the robustness of the robot's performance in a great variety of tasks including localization, tracking, and map building. In this paper, we present a novel probabilistic measurement model for range finders, called Gaussian Beam Processes, which treats the measurement modeling task as a nonparametric Bayesian regression problem and solves it using Gaussian processes. The major advantage of our approach lies in the smoothness of the resulting model which appropriately represents correlations between adjacent beams using covariance functions. Moreover, the Gaussian process treatment results in a sound probabilistic measurement model with a pool of well-established techniques for likelihood estimation and range prediction for an arbitrary number of beams. Experiments on real world and synthetic data show that Gaussian Beam Processes combine the advantages of two popular measurement models.},
}