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.

翻译：本文统一了同步定位与地图构建框架中用于鲁棒离群点检测的一致集最大化理论。我们首先描述成对一致性的概念，进而讨论如何通过评估测量对的一致性构建一致性图。寻找最大一致测量集转化为最大团问题的实例，并可通过现有最大团求解器相对快速地求解。随后，我们通过使用泛化的一致性概念和泛化图，将算法推广到基于群-$k$的一致性校验。我们同时提出了能在泛化图上运行的改进最大团算法，用于寻找内部满足群-$k$一致性的测量集。针对群-$k$一致性的指数级复杂度问题，我们给出了能显著减少一致性评估所需校验次数的方法。在仿真与硬件实验中，我们将先前工作扩展至多智能体系统，并与其它前沿方法进行了对比。