Monday, August 23, 2010

Lab Meeting August 23rd, 2010 (ShaoChen): Distributed Nonlinear Estimation for Robot Localization using Weighted Consensus (ICRA'10)

Title: Distributed Nonlinear Estimation for Robot Localization using Weighted Consensus

Authors: Andrea Simonetto, Tam´as Keviczky and Robert Babuˇska

Abstract:

 Distributed linear estimation theory has received increased
attention  in  recent  years  due  to  several  promising
industrial applications. Distributed nonlinear estimation, however
is  still  a  relatively  unexplored  field  despite  the  need  in
numerous practical situations for techniques that can handle
nonlinearities. This paper presents a unified way of describing
distributed implementations of three commonly used nonlinear
estimators: the Extended Kalman Filter, the Unscented Kalman
Filter  and  the  Particle  Filter.  Leveraging  on  the  presented
framework,  we  propose  new  distributed  versions  of  these
methods, in which the nonlinearities are locally managed by
the various sensors whereas the different estimates are merged
based on a weighted average consensus process. The proposed
versions are shown to outperform the few published ones in
two robot localization test cases.

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