Cooperative Robot Localization Using Event-Triggered Estimation

This paper describes a novel communication-spare cooperative localization algorithm for a team of mobile unmanned robotic vehicles. Exploiting an event-based estimation paradigm, robots only send measurements to neighbors when the expected innovation for state estimation is high. Because agents know the event-triggering condition for measurements to be sent, the lack of a measurement is thus also informative and fused into state estimates. The robots use a covariance intersection mechanism to occasionally synchronize their local estimates of the full network state. In addition, heuristic balancing dynamics on the robots' covariance-intersection-triggering thresholds ensure that, in large-diameter networks, the local error covariances remains below desired bounds across the network. Simulations on both linear and nonlinear dynamics/measurement models show that the event-triggering approach achieves nearly optimal state estimation performance in a wide range of operating conditions, even when using only a fraction of the communication cost required by conventional full data sharing. The robustness of the proposed approach to lossy communications as well as the relationship between network topology and covariance-intersection-based synchronization requirements are also examined.