  Due to the distributed nature of cooperative simultaneous localization and
mapping (CSLAM), detecting inter-robot loop closures necessitates sharing
sensory data with other robots. A na\"{\i}ve approach to data sharing can
easily lead to a waste of mission-critical resources. This paper investigates
the logistical aspects of CSLAM. Particularly, we present a general
resource-efficient communication planning framework that takes into account
both the total amount of exchanged data and the induced division of labor
between the participating robots. Compared to other state-of-the-art
approaches, our framework is able to verify the same set of potential
inter-robot loop closures while exchanging considerably less data and
influencing the induced workloads. We develop a fast algorithm for finding
globally optimal communication policies, and present theoretical analysis to
characterize the necessary and sufficient conditions under which simpler
strategies are optimal. The proposed framework is extensively evaluated with
data from the KITTI odometry benchmark datasets.
