Robotics: Science and Systems XIV

Near-Optimal Budgeted Data Exchange for Distributed Loop Closure Detection

Yulun Tian, Kasra Khosoussi, Matthew Giamou, Jonathan How, Jonathan Kelly


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.



    AUTHOR    = {Yulun Tian AND Kasra Khosoussi AND Matthew Giamou AND Jonathan How AND Jonathan Kelly}, 
    TITLE     = {Near-Optimal Budgeted Data Exchange for Distributed Loop Closure Detection}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2018}, 
    ADDRESS   = {Pittsburgh, Pennsylvania}, 
    MONTH     = {June}, 
    DOI       = {10.15607/RSS.2018.XIV.071}