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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