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Uncertainty Minimization in Multi-sensor Localization Systems Using Model Selection Theory

Summary: Uncertainty Minimization in Multi-sensor Localization Systems
Using Model Selection Theory
Sreenivas R. Sukumar, Hamparsum Bozdogan, David L. Page, Andreas F. Koschan, Mongi A. Abidi
Imaging, Robotics and Intelligent Systems Lab, The University of Tennessee, Knoxville.
{ssrangan, bozdogan, dpage, akoschan, abidi}@utk.edu
Belief propagation methods are the state-of-the-art
with multi-sensor state localization problems.
However, when localization applications have to deal
with multi-modality sensors whose functionality
depends on the environment of operation, we
understand the need for an inference framework to
identify confident and reliable sensors. Such a
framework helps eliminate failed/non-functional
sensors from the fusion process minimizing uncertainty
while propagating belief. We derive a framework
inspired from model selection theory and demonstrate
results on real world multi-sensor robot state
localization and multi-camera target tracking


Source: Abidi, Mongi A. - Department of Electrical and Computer Engineering, University of Tennessee
Koschan, Andreas - Imaging, Robotics, and Intelligent Systems, University of Tennessee


Collections: Computer Technologies and Information Sciences; Engineering