  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. Since 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 (CI) mechanism to occasionally synchronize their
local estimates of the full network state. In addition, heuristic balancing
dynamics on the robots' CI-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 CI-based synchronization
requirements, are also examined.
