  Pose-graph SLAM is the de facto standard framework for constructing
large-scale maps from multi-session experiences of relative observations and
motions during visual robot navigation. It has received increasing attention in
the context of recent advanced SLAM frameworks such as graph neural SLAM. One
remaining challenge is landmark misrecognition errors (i.e., incorrect landmark
edges) that can have catastrophic effects on the inferred pose-graph map. In
this study, we present comprehensive criteria to maximize global consistency in
the pose graph using a new robust graph cut technique. Our key idea is to
formulate the problem as a minimum-cost multi-cut that enables us to optimize
not only landmark correspondences but also the number of landmarks while
allowing for a varying number of landmarks. This makes our proposed approach
invariant against the type of landmark measurement, graph topology, and metric
information, such as the speed of the robot motion. The proposed graph cut
technique was integrated into a practical SLAM framework and verified
experimentally using the public NCLT dataset.
