@InProceedings{Clemens2014,
  author    = {Joachim Clemens and Thomas Reineking},
  title     = {Multi-Sensor Fusion Using Evidential {SLAM} for Navigating a Probe through Deep Ice},
  booktitle = {Belief Functions: Theory and Applications},
  publisher = {Springer Science + Business Media},
  year      = {2014},
  pages     = {339--347},
	title     = {Multi-Sensor Fusion Using Evidential {SLAM} for Navigating a Probe through Deep Ice},
	volume    = {8764},
	series    = {Lecture Notes in Computer Science},
	editor    = {Cuzzolin, Fabio},
  abstract  = {We present an evidential multi-sensor fusion approach for navigating a maneuverable ice probe designed for extraterrestrial sample analysis missions. The probe is equipped with a variety of sensors and has to estimate its own position within the ice as well as a map of its surroundings. The sensor fusion is based on an evidential SLAM approach which produces evidential occupancy grid maps that contain more information about the environment compared to probabilistic grid maps. We describe the different sensor models underlying the algorithm and we present empirical results obtained under controlled conditions in order to analyze the effectiveness of the proposed multi-sensor fusion approach. In particular, we show that the localization error is significantly reduced by combining multiple sensors.},
  doi       = {10.1007/978-3-319-11191-9_37},
  url       = {10.1007/978-3-319-11191-9_37">http://dx.doi.org/10.1007/978-3-319-11191-9_37},
}