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This paper presents resource-aware algorithms for distributed inter-robotloop closure detection for applications such as collaborative simultaneouslocalization and mapping (CSLAM) and distributed image retrieval. In real-worldscenarios, this process is resource-intensive as it involves exchanging manyobservations and geometrically verifying a large number of potential matches.This poses severe challenges for small-size and low-cost robots with variousoperational and resource constraints that limit, e.g., energy consumption,communication bandwidth, and computation capacity. This paper proposes aframework in which robots first exchange compact queries to identify a set ofpotential loop closures. We then seek to select a subset of potentialinter-robot loop closures for geometric verification that maximizes a monotonesubmodular performance metric without exceeding budgets on computation (numberof geometric verifications) and communication (amount of exchanged data forgeometric verification). We demonstrate that this problem is in generalNP-hard, and present efficient approximation algorithms with provableperformance guarantees. The proposed framework is extensively evaluated on realand synthetic datasets. A natural convex relaxation scheme is also presented tocertify the near-optimal performance of the proposed framework in practice.

A Resource-Aware Approach to Collaborative Loop Closure Detection with Provable Performance Guarantees