k-一様ハイパーグラフ上の最大クリークによる群k-整合計測集合最大化とロバストなマルチロボット地図統合
Group-$k$ consistent measurement set maximization via maximum clique over k-Uniform hypergraphs for robust multi-robot map merging
SLAMにおける外れ値除去のための整合集合最大化を群k整合に一般化し、k-一様ハイパーグラフ上の最大クリーク問題として解くことで、マルチロボット地図統合をロバスト化する手法を提案。
著者: Brendon Forsgren, Ram Vasudevan, Michael Kaess, Timothy W. McLain, Joshua G. Mangelson
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
This paper unifies the theory of consistent-set maximization for robust outlier detection in a simultaneous localization and mapping framework. We first describe the notion of pairwise consistency before discussing how a consistency graph can be formed by evaluating pairs of measurements for consistency. Finding the largest set of consistent measurements is transformed into an instance of the maximum clique problem and can be solved relatively quickly using existing maximum-clique solvers. We then generalize our algorithm to check consistency on a group-$k$ basis by using a generalized notion of consistency and using generalized graphs. We also present modified maximum clique algorithms that function on generalized graphs to find the set of measurements that is internally group-$k$ consistent. We address the exponential nature of group-$k$ consistency and present methods that can substantially decrease the number of necessary checks performed when evaluating consistency. We extend our prior work to multi-agent systems in both simulation and hardware and provide a comparison with other state-of-the-art methods.
