Uncertainty in Artificial Intelligence
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Bayes Networks for Sonar Sensor Fusion
Ami Berler, Solomon Shimony
Abstract:
Wide-angle sonar mapping of the environment by mobile robot is nontrivial due to several sources of uncertainty: dropouts due to "specular" reflections, obstacle location uncertainty due to the wide beam, and distance measurement error. Earlier papers address the latter problems, but dropouts remain a problem in many environments. We present an approach that lifts the overoptimistic independence assumption used in earlier work, and use Bayes nets to represent the dependencies between objects of the model. Objects of the model consist of readings, and of regions in which "quasi location invariance" of the (possible) obstacles exists, with respect to the readings. Simulation supports the method's feasibility. The model is readily extensible to allow for prior distributions, as well as other types of sensing operations.
Keywords: Sensor fusion, sonar-based mapping, probabilistic reasoning, Bayes nets.
Pages: 14-21
PS Link: http://www.cs.bgu.ac.il/~shimony/sonars.ps.Z
PDF Link: /papers/97/p14-berler.pdf
BibTex:
@INPROCEEDINGS{Berler97,
AUTHOR = "Ami Berler and Solomon Shimony",
TITLE = "Bayes Networks for Sonar Sensor Fusion",
BOOKTITLE = "Proceedings of the Thirteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-97)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1997",
PAGES = "14--21"
}


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