Group-$k$ Consistent Measurement Set Maximization for Robust Outlier Detection
Brendon Forsgren, Ram Vasudevan, Michael Kaess, Timothy W. McLain, Joshua G. Mangelson · 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · 2022
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 ($\mathrm{G}k\text{CM}$) 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$\mathrm{G}k\text{CM}$using simulated data and compares it to pairwise consistency maximization (PCM) presented in previous work.