In SLAM (Simultaneous localization and mapping) problems, Pose Graph Optimization (PGO) is a technique to refine an initial estimate of a set of poses (positions and orientations) from a set of pairwise relative measurements. The optimization procedure can be negatively affected even by a single outlier measurement, with possible catastrophic and meaningless results. Although recent works on robust optimization aim to mitigate the presence of outlier measurements, robust solutions capable of handling large numbers of outliers are yet to come. This paper presents IPC, acronym for Incremental Probabilistic Consensus, a method that approximates the solution to the combinatorial problem of finding the maximally consistent set of measurements in an incremental fashion. It evaluates the consistency of each loop closure measurement through a consensus-based procedure, possibly applied to a subset of the global problem, where all previously integrated inlier measurements have veto power. We evaluated IPC on standard benchmarks against several state-of-the-art methods. Although it is simple and relatively easy to implement, IPC competes with or outperforms the other tested methods in handling outliers while providing online performances. We release with this paper an open-source implementation of the proposed method.

翻译：在SLAM（同步定位与建图）问题中，位姿图优化（PGO）是一种通过成对相对测量数据来优化初始位姿（位置与方向）估计的技术。即使存在单个异常值测量，优化过程也可能受到负面影响，导致可能产生灾难性且无意义的结果。尽管近期关于鲁棒优化的研究旨在减轻异常值测量的影响，但能够处理大量异常值的鲁棒解决方案仍有待突破。本文提出IPC（增量概率共识的缩写），该方法以增量方式逼近寻找最大一致性测量集合这一组合问题的解。它通过基于共识的流程评估每个闭环测量的一致性，该流程可应用于全局问题的子集，其中所有先前已整合的内点测量均具有否决权。我们在标准基准测试中将IPC与多种先进方法进行比较评估。尽管IPC方法简单且相对易于实现，但在处理异常值方面，它与其它测试方法相比具有竞争力或更优性能，同时提供在线处理能力。我们随本文发布了该方法的开源实现。