  Inter-robot loop closure detection, e.g., for collaborative simultaneous
localization and mapping (CSLAM), is a fundamental capability for many
multirobot applications in GPS-denied regimes. In real-world scenarios, this is
a resource-intensive process that involves exchanging observations and
verifying potential matches. This poses severe challenges especially for
small-size and low-cost robots with various operational and resource
constraints that limit, e.g., energy consumption, communication bandwidth, and
computation capacity. This paper presents resource-aware algorithms for
distributed inter-robot loop closure detection. In particular, we seek to
select a subset of potential inter-robot loop closures that maximizes a
monotone submodular performance metric without exceeding computation and
communication budgets. We demonstrate that this problem is in general NP-hard,
and present efficient approximation algorithms with provable performance
guarantees. A convex relaxation scheme is used to certify near-optimal
performance of the proposed framework in real and synthetic SLAM benchmarks.
