Scalable Event-Triggered Data Fusion for Autonomous Cooperative Swarm Localization

We present a Kalman filter-based method for event-triggered decentralized data fusion in cooperative localization problems that can be scaled to large numbers of networked autonomous robots operating in a swarm. This scaling is accomplished without significantly increasing the number of states each robot must estimate locally or the amount of data transferred through the network, thanks to implicit measurement fusion updates afforded by event-triggered communication. To keep state estimates suitably synchronized in this event-triggered approach, we exploit a novel method for performing reduced order partial covariance intersection between local estimates, such that only the overlapping set of swarm states between any two communicating agents' estimates need to be fused. This reduces the number of swarm states each agent must track to only the subset of swarm states that have a significant effect on that agent's 'ownship' state estimates. This leads to significantly simpler onboard computation for each agent and reduces the amount of data that must be transferred through the network when synchronization and measurement messages are considered together. Cooperative localization simulations for a 30 robot swarm show that the method is able to achieve good localization performance, even for greatly reduced measurement sharing and greatly reduced overall data transfer compared to full state decentralized fusion.