IPC：面向SLAM后端的基于增量概率一致性的一致集最大化方法
IPC: Incremental Probabilistic Consensus-based Consistent Set Maximization for SLAM Backends
AI总结：
针对SLAM中位姿图优化易受大量异常测量值影响的问题，提出增量概率一致性方法IPC，通过带内点否决权的一致性流程增量求解最大一致测量集，在标准基准上性能优于或比肩现有方法且支持在线运行，已开源。
摘要：
在SLAM（同步定位与地图构建）问题中，Pose Graph Optimization（位姿图优化，PGO）是一种根据成对相对测量值来优化一组位姿（位置和朝向）初始估计的技术。即便只有单个异常测量值，也可能对优化过程产生负面影响，甚至导致灾难性的无效结果。尽管近期关于鲁棒优化的研究致力于缓解异常测量值的影响，但目前仍缺乏能处理大量异常值的鲁棒解决方案。本文提出IPC（Incremental Probabilistic Consensus，增量概率一致性）方法，该方法以增量方式近似求解寻找最大一致测量集的组合问题。它通过基于一致性的流程评估每个回环测量值的一致性，该流程可应用于全局问题的子集，且所有先前整合的内点测量值都拥有否决权。我们在标准基准数据集上将IPC与多种当前最优方法进行了对比。尽管IPC原理简单、实现难度较低，但它在异常值处理能力上与其他被测方法相当甚至更优，同时还能保证在线运行性能。我们随本文公开了所提方法的开源实现代码。
英文摘要：
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.
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