  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.
