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“Group-k Consistent Measurement Set Maximization for Robust Outlier Detection” by B. Forsgren, R. Vasudevan, M. Kaess, T.W. McLain, and J.G. Mangelson. In Proc. IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems, IROS, (Kyoto, Japan), Oct. 2022.
This paper presents a method for 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 (GkCM) 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 GkCM using simulated data and compares it to pairwise consistency maximization (PCM) presented in previous work.
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BibTeX entry:
@inproceedings{Forsgren22iros,
author = {B. Forsgren and R. Vasudevan and M. Kaess and T.W. McLain and
J.G. Mangelson},
title = {Group-k Consistent Measurement Set Maximization for Robust
Outlier Detection},
booktitle = {Proc. IEEE/RSJ Intl. Conf. on Intelligent Robots and
Systems, IROS},
address = {Kyoto, Japan},
month = oct,
year = {2022}
}