Cooperative Localization under Limited Connectivity
This work addresses the challenge of enabling robust localization for mobile agents in scenarios with restricted communication, representing an incremental improvement over existing methods by focusing on reduced connectivity requirements.
The paper tackles the problem of decentralized multi-agent cooperative localization under limited connectivity by introducing two algorithms that avoid maintaining inter-agent correlations to reduce communication costs, achieving filter consistency through covariance bounding and optimization methods, and demonstrating effectiveness in simulation and robotic experiments.
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.