Journal
IEEE TRANSACTIONS ON ROBOTICS
Volume 35, Issue 6, Pages 1523-1530Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TRO.2019.2930404
Keywords
Agent-based system; decentralized cooperative localization; networked robots
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Funding
- U.S. Dept. of Commerce, National Institute of Standards Technology Award [70NANB17H192]
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In this article, we report two decentralized multiagent cooperative localization algorithms in which, to reduce the communication cost, interagent state estimate correlations are not maintained but accounted for implicitly. In our first algorithm, to guarantee filter consistency, we account for unknown interagent correlations via an upper bound on the joint covariance matrix of the agents. In the second method, we use an optimization framework to estimate the unknown interagent cross-covariance matrix. In our algorithms, each agent localizes itself in a global coordinate frame using a local filter driven by local dead reckoning and occasional absolute measurement updates, and opportunistically corrects its pose estimate whenever it can obtain relative measurements with respect to other mobile agents. To process any relative measurement, only the agent that has taken the measurement and the agent the measurement is taken from need to communicate with each other. Consequently, our algorithms are decentralized algorithms that do not impose restrictive network-wide connectivity condition. Moreover, we make no assumptions about the type of agents or relative measurements. We demonstrate our algorithms in simulation and a roboticexperiment.
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