  This paper considers the problem of cooperative localization (CL) using
inter-robot measurements for a group of networked robots with limited on-board
resources. We propose a novel recursive algorithm in which each robot localizes
itself in a global coordinate frame by local dead reckoning, and
opportunistically corrects its pose estimate whenever it receives a relative
measurement update message from a server. The computation and storage cost per
robot in terms of the size of the team is of order O(1), and the robots are
only required to transmit information when they are involved in a relative
measurement. The server also only needs to compute and transmit update messages
when it receives an inter-robot measurement. We show that under perfect
communication, our algorithm is an alternative but exact implementation of a
joint CL for the entire team via Extended Kalman Filter (EKF). The perfect
communication however is not a hard requirement. In fact, we show that our
algorithm is intrinsically robust with respect to communication failures, with
formal guarantees that the updated estimates of the robots receiving the update
message are of minimum variance in a first-order approximate sense at that
given timestep. We demonstrate the performance of the algorithm in simulation
and experiments.
