Dimensions of Uncertainty in Evidential Grid Maps
by ,
Abstract:
We show how a SLAM algorithm based on belief function theory can produce evidential occupancy grid maps that provide a mobile robot with additional information about its environment. While uncertainty in probabilistic grid maps is usually measured by entropy, we show that for evidential grid maps, uncertainty can be expressed in a three-dimensional space and we propose appropriate measures for quantifying uncertainty in these different dimensions. We analyze these measures in a practical mapping example containing typical sources of uncertainty for SLAM. As a result of the evidential representation, the robot is able to distinguish between different sources of uncertainty (e.g., a lack of measurements vs. conflicting measurements) which are indistinguishable in the probabilistic framework.
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PDF URL: http://dx.doi.org/10.1007/978-3-319-11215-2_20
Reference:
Dimensions of Uncertainty in Evidential Grid Maps (Thomas Reineking, Joachim Clemens), In Spatial Cognition IX (Christian Freksa, Bernhard Nebel, Mary Hegarty, Thomas Barkowsky, eds.), Springer Science + Business Media, volume 8684, 2014.
Bibtex Entry:
@InProceedings{Reineking2014a,
  author    = {Thomas Reineking and Joachim Clemens},
  title     = {Dimensions of Uncertainty in Evidential Grid Maps},
  booktitle = {Spatial Cognition {IX}},
  publisher = {Springer Science + Business Media},
  year      = {2014},
	volume    = {8684},
	editor    = {Freksa, Christian and Nebel, Bernhard and Hegarty, Mary and Barkowsky, Thomas},
	series    = {Lecture Notes in Computer Science},
  pages     = {283--298},
  abstract  = {We show how a SLAM algorithm based on belief function theory can produce evidential occupancy grid maps that provide a mobile robot with additional information about its environment. While uncertainty in probabilistic grid maps is usually measured by entropy, we show that for evidential grid maps, uncertainty can be expressed in a three-dimensional space and we propose appropriate measures for quantifying uncertainty in these different dimensions. We analyze these measures in a practical mapping example containing typical sources of uncertainty for SLAM. As a result of the evidential representation, the robot is able to distinguish between different sources of uncertainty (e.g., a lack of measurements vs. conflicting measurements) which are indistinguishable in the probabilistic framework.},
  doi       = {10.1007/978-3-319-11215-2_20},
  url       = {10.1007/978-3-319-11215-2_20">http://dx.doi.org/10.1007/978-3-319-11215-2_20},
}