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（同步定位与地图构建）问题中，位姿图优化（PGO）是一种通过一组成对相对测量值来精化初始位姿（位置和方向）估计的技术。即使单个异常测量值也可能对优化过程产生负面影响，甚至导致灾难性的无意义结果。尽管近年来鲁棒优化领域的研究工作致力于减轻异常测量值的影响，但能够处理大量异常值的鲁棒解决方案仍有待开发。本文提出IPC（增量概率一致性）方法，该方法通过增量方式近似求解寻找最大一致测量集这一组合优化问题。它通过基于一致性的过程评估每个回环闭合测量值的一致性（可应用于全局问题的子集），其中所有先前已集成的内点测量值具有否决权。我们在标准基准数据集上，将IPC与多种先进方法进行了对比评估。虽然IPC方法简单且易于实现，但在处理异常值方面能与其它测试方法相媲美甚至更优，同时提供在线性能。本文同时开放了所提方法的源代码实现。