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arXiv · 2209.02658

Group-$k$ Consistent Measurement Set Maximization for Robust Outlier Detection

Brendon Forsgren·Ram Vasudevan·Michael Kaess·Timothy W. McLain·Joshua G. Mangelson
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Abstract

This paper presents a method for the robust selection of measurements in a simultaneous localization and mapping (SLAM) framework. Existing methods check consistency or compatibility on a pairwise basis, however many measurement types are not sufficiently constrained in a pairwise scenario to determine if either measurement is inconsistent with the other. This paper presents group-$k$ consistency maximization (G$k$CM) that estimates the largest set of measurements that is internally group-$k$ consistent. Solving for the largest set of group-$k$ consistent measurements can be formulated as an instance of the maximum clique problem on generalized graphs and can be solved by adapting current methods. This paper evaluates the performance of G$k$CM using simulated data and compares it to pairwise consistency maximization (PCM) presented in previous work.

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Brendon Forsgren, Ram Vasudevan, Michael Kaess, Timothy W. McLain, Joshua G. Mangelson. 2022-09-06. Group-$k$ Consistent Measurement Set Maximization for Robust Outlier Detection. https://arxiv.org/abs/2209.02658

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