  Inter-robot loop closure detection is a core problem in collaborative SLAM
(CSLAM). Establishing inter-robot loop closures is a resource-demanding
process, during which robots must consume a substantial amount of
mission-critical resources (e.g., battery and bandwidth) to exchange sensory
data. However, even with the most resource-efficient techniques, the resources
available onboard may be insufficient for verifying every potential loop
closure. This work addresses this critical challenge by proposing a
resource-adaptive framework for distributed loop closure detection. We seek to
maximize task-oriented objectives subject to a budget constraint on total data
transmission. This problem is in general NP-hard. We approach this problem from
different perspectives and leverage existing results on monotone submodular
maximization to provide efficient approximation algorithms with performance
guarantees. The proposed approach is extensively evaluated using the KITTI
odometry benchmark dataset and synthetic Manhattan-like datasets.
