  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 track its own
state and autocovariance and compensate for the unknown correlations between
robots. Two novel different methods for utilizing common environmental temporal
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 naive non-realtime centralized counterpart
extensively in both simulations and real-world experiments.
