  This paper studies the measurement scheduling problem for a group of N mobile
robots moving on a flat surface that are preforming cooperative localization
(CL). We consider a scenario in which due to the limited on-board resources
such as battery life and communication bandwidth only a given number of
relative measurements per robot are allowed at observation and update stage.
Optimal selection of which teammates a robot should take a relative measurement
from such that the updated joint localization uncertainty of the team is
minimized is an NP-hard problem. In this paper, we propose a suboptimal greedy
approach that allows each robot to choose its landmark robots locally in
polynomial time. Our method, unlike the known results in the literature, does
not assume full-observability of CL algorithm. Moreover, it does not require
inter-robot communication at scheduling stage. That is, there is no need for
the robots to collaborate to carry out the landmark robot selections. We
discuss the application of our method in the context of an state-of-the-art
decentralized CL algorithm and demonstrate its effectiveness through numerical
simulations. Even though our solution does not come with rigorous performance
guarantees, its low computational cost along with no communication requirement
makes it an appealing solution for operatins with resource constrained robots.
