@InProceedings{pfaff08iros,
  title     = {Efficiently Learning High-dimensional Observation Models
    for Monte-Carlo Localization using Gaussian Mixtures},
  author    = {Pfaff, P. and Stachniss, C. and Plagemann, C. and Burgard, W.},
  booktitle = {Proc.~of the IEEE/RSJ International Conference on
    Intelligent Robots and Systems (IROS)},
  address   = {Nice, France},
  year      = {2008},
  abstract  = {
Whereas probabilistic approaches are a powerful tool for mobile robot
localization, they heavily rely on the proper definition of the
so-called observation model which defines the likelihood of an
observation given the position and orientation of the robot and the
map of the environment. Most of the sensor models for range sensors
proposed in the past either consider the individual beam measurements
independently or apply uni-modal models to represent the likelihood
function. In this paper we present an approach that learns
place-dependent sensor models for entire range scans using Gaussian
mixture models. To deal with the high dimensionality of the
measurement space, we utilize principle component analysis for
dimensionality reduction. In practical experiments carried out with
data obtained from a real robot we demonstrate that our model
substantially outperforms existing and popular sensor models.
},
  note      = {To appear},
  pdfurl    = {http://www.informatik.uni-freiburg.de/~plagem/bib/pfaff08iros.pdf}
}