Title: Distributed Multirobot Localization(IEEE Transactions on Robotics and Automation Oct. 2002)
Authors: Stergios I. Roumeliotis, George A. Bekey
Abstract:
In this paper, we present a new approach to the
problem of simultaneously localizing a group of mobile robots
capable of sensing one another. Each of the robots collects sensor
data regarding its own motion and shares this information with
the rest of the team during the update cycles. A single estimator,
in the form of a Kalman filter, processes the available positioning
information from all the members of the team and produces
a pose estimate for every one of them. The equations for this
centralized estimator can be written in a decentralized form,
therefore allowing this single Kalman filter to be decomposed
into a number of smaller communicating filters. Each of these
filters processes sensor data collected by its host robot. Exchange
of information between the individual filters is necessary only
when two robots detect each other and measure their relative
pose. The resulting decentralized estimation schema, which we
call collective localization, constitutes a unique means for fusing
measurements collected from a variety of sensors with minimal
communication and processing requirements. The distributed
localization algorithm is applied to a group of three robots and
the improvement in localization accuracy is presented. Finally, a
comparison to the equivalent decentralized information filter is
provided.
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