Pairwise Consistent Measurement Set Maximization for Robust Multi-Robot Map Merging
Pairwise Consistent Measurement Set Maximization for Robust Multi-Robot Map Merging
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DOI：
10.1109/icra.2018.8460217
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发表时间：
2018-05
期刊：
影响因子：
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通讯作者：
Joshua G. Mangelson;Derrick Dominic;R. Eustice;Ram Vasudevan
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文献类型：
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作者：
Joshua G. Mangelson;Derrick Dominic;R. Eustice;Ram Vasudevan
提出了一种多机器人同时定位与地图构建（SLAM）中地图间环闭包鲁棒选择的方法。现有的鲁棒SLAM方法假设良好的初始化或“里程骨干”来分类内点和离群点循环闭合。在多机器人的情况下，这些假设并不总是成立。本文提出了一种算法，称为成对一致性最大化（PCM），估计最大的成对内部一致的测量集。寻找最大的成对内部一致集可以转化为图论中的最大团问题的一个实例，并且通过利用相关文献可以实时解决。本文评估如何以及PCM近似组合金标准使用模拟数据。它还评估了PCM的性能与DCS，SCGP和RANSAC相比，在合成和真实世界的数据集，并表明PCM显着优于这些方法。
This paper reports on a method for robust selection of inter-map loop closures in multi-robot simultaneous localization and mapping (SLAM). Existing robust SLAM methods assume a good initialization or an “odometry backbone” to classify inlier and outlier loop closures. In the multi-robot case, these assumptions do not always hold. This paper presents an algorithm called Pairwise Consistency Maximization (PCM) that estimates the largest pairwise internally consistent set of measurements. Finding the largest pairwise internally consistent set can be transformed into an instance of the maximum clique problem from graph theory, and by leveraging the associated literature it can be solved in realtime. This paper evaluates how well PCM approximates the combinatorial gold standard using simulated data. It also evaluates the performance of PCM on synthetic and real-world data sets in comparison with DCS, SCGP, and RANSAC, and shows that PCM significantly outperforms these methods.