分散ループ閉じ込め検出のための予算制約付きデータ交換の準最適化
Near-Optimal Budgeted Data Exchange for Distributed Loop Closure Detection
協調SLAMにおけるロボット間ループ閉じ込め検出を、総データ送信量の予算制約下でタスク目的を最大化する問題として定式化し、単調劣モジュラ最大化に基づく近似アルゴリズムで効率的に解く手法を提案した。
著者: Yulun Tian, Kasra Khosoussi, Matthew Giamou, Jonathan P. How, Jonathan Kelly
分類: cs.RO, cs.DS
原文アブストラクト
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.
