計算・通信資源を考慮した協調ループ閉じ込め検出と性能保証付き近似アルゴリズム
A Resource-Aware Approach to Collaborative Loop Closure Detection with Provable Performance Guarantees
複数ロボット間のループ閉じ込め検出において、計算量と通信量の予算内で検証対象を選別する劣モジュラ最適化フレームワークを提案し、NP困難性を示した上で性能保証付きの近似解法を実データと合成データで評価した。
著者: Yulun Tian, Kasra Khosoussi, Jonathan P. How
分類: cs.RO, cs.DS
原文アブストラクト
This paper presents resource-aware algorithms for distributed inter-robot loop closure detection for applications such as collaborative simultaneous localization and mapping (CSLAM) and distributed image retrieval. In real-world scenarios, this process is resource-intensive as it involves exchanging many observations and geometrically verifying a large number of potential matches. This poses severe challenges for small-size and low-cost robots with various operational and resource constraints that limit, e.g., energy consumption, communication bandwidth, and computation capacity. This paper proposes a framework in which robots first exchange compact queries to identify a set of potential loop closures. We then seek to select a subset of potential inter-robot loop closures for geometric verification that maximizes a monotone submodular performance metric without exceeding budgets on computation (number of geometric verifications) and communication (amount of exchanged data for geometric verification). We demonstrate that this problem is in general NP-hard, and present efficient approximation algorithms with provable performance guarantees. The proposed framework is extensively evaluated on real and synthetic datasets. A natural convex relaxation scheme is also presented to certify the near-optimal performance of the proposed framework in practice.
