Event-based cooperative localization using implicit and explicit measurements

This paper describes a novel cooperative localization algorithm for a team of robotic agents to estimate the state of the network via local communications. Exploiting an event-based paradigm, agents only send measurements to their neighbors when the expected benefit to employ this information is high. Because agents know the event-triggering condition for measurements to be sent, the lack of a measurement is also informative and fused into state estimates. For the case where agents do not receive direct measurements of all others and keep a local-covariance error metric bounded, the agents employ a Covariance Intersection fusion rule. In communication networks with large diameters, it may not be the case that triggering fusion updates when the error metric passes a threshold results into a posterior metric satisfying the desired bound. Thus, we define balancing dynamics on the robots´ triggering thresholds that result into more central agents updating more often to aid the less connected ones. Simulations illustrate the effectiveness of this approach.