  In this paper we present a consistent and distributed state estimator for
multi-robot cooperative localization (CL) which efficiently fuses environmental
features and loop-closure constraints across time and robots. In particular, we
leverage covariance intersection (CI) to allow each robot to only estimate its
own state and autocovariance and compensate for the unknown correlations
between robots. Two novel multi-robot methods for utilizing common
environmental SLAM features are introduced and evaluated in terms of accuracy
and efficiency. Moreover, we adapt CI to enable drift-free estimation through
the use of loop-closure measurement constraints to other robots' historical
poses without a significant increase in computational cost. The proposed
distributed CL estimator is validated against its non-realtime centralized
counterpart extensively in both simulations and real-world experiments.
