  In cooperative localization, communicating mobile agents use inter-agent
relative measurements to improve their dead-reckoning-based global
localization. Measurement scheduling enables an agent to decide which subset of
available inter-agent relative measurements it should process when its
computational resources are limited. Optimal measurement scheduling is an
NP-hard combinatorial optimization problem. The so-called sequential greedy
(SG) algorithm is a popular suboptimal polynomial-time solution for this
problem. However, the merit function evaluation for the SG algorithms requires
access to the state estimate vector and error covariance matrix of all the
landmark agents (teammates that an agent can take measurements from). This
paper proposes a measurement scheduling for CL that follows the SG approach but
reduces the communication and computation cost by using a neural network-based
surrogate model as a proxy for the SG algorithm's merit function. The
significance of this model is that it is driven by local information and only a
scalar metadata from the landmark agents. This solution addresses the time and
memory complexity issues of running the SG algorithm in three ways: (a)
reducing the inter-agent communication message size, (b) decreasing the
complexity of function evaluations by using a simpler surrogate (proxy)
function, (c) reducing the required memory size.Simulations demonstrate our
results.
