Near-Optimal Budgeted Data Exchange for Distributed Loop Closure Detection
This work addresses a critical resource bottleneck for robots in collaborative SLAM, though it is incremental as it builds on existing monotone submodular maximization techniques.
The paper tackles the resource-intensive challenge of inter-robot loop closure detection in collaborative SLAM by proposing a resource-adaptive framework that maximizes task objectives under a data transmission budget, using approximation algorithms with performance guarantees and evaluating them on KITTI and synthetic datasets.
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.