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.