Cooperative Localization under Limited Connectivity

TL;DR

This paper introduces two decentralized multi-agent cooperative localization algorithms that reduce communication costs by implicitly handling inter-agent correlations, suitable for limited connectivity scenarios, demonstrated through simulations and robotic experiments.

Contribution

The paper presents novel decentralized algorithms that implicitly account for inter-agent correlations without requiring network-wide connectivity or specific agent types.

Findings

Algorithms maintain filter consistency with limited communication.

Effective in simulation and robotic experiments.

No assumptions on agent types or measurement modalities.

Abstract

We report two decentralized multi-agent cooperative localization algorithms in which, to reduce the communication cost, inter-agent state estimate correlations are not maintained but accounted for implicitly. In our first algorithm, to guarantee filter consistency, we account for unknown inter-agent 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 inter-agent 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 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 robotic~experiment.