@InProceedings{Reineking2013,
  author    = {T. Reineking and J. Clemens},
  title     = {Evidential {FastSLAM} for Grid Mapping},
  booktitle = {16th International Conference on Information Fusion (FUSION)},
  year      = {2013},
  pages     = {789-796},
  month     = {jul},
  abstract  = {We present a solution to the problem of simultaneous localization and mapping (SLAM) based on Dempster-Shafer theory. While several works on the mapping problem based on belief functions exist, none of these approaches deal with the full SLAM problem. In this paper, we derive an evidential version of the FastSLAM algorithm based on a Rao-blackwellized particle filter where belief functions are used for representing a grid map of the robot's environment. The resulting algorithm includes the probabilistic FastSLAM solution as a special case while preserving its low computational complexity. Due to the additional dimensions of uncertainty provided by belief functions, we obtain maps that explicitly show missing information and conflicting sensor measurements. We evaluate our approach using simulation and a real robot equipped with sonar sensors by comparing maps generated by different combination rules, including Bayesian updating.},
  organization = {ISIF},
	url				= {http://ieeexplore.ieee.org/document/6641073/}
}