Distributed nonlinear estimation for robot localization using weighted consensus

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.