Pairwise Consistent Measurement Set Maximization for Robust Multi-robot Map Merging

Joshua G Mangelson, Derrick Dominic, Ryan Eustice, Ram Vasudevan

Abstract:

Abstract— This paper reports on a method for robust selection of inter-map loop closures in multi-robot simultaneous
localization and mapping (SLAM). Existing robust SLAM
methods assume a good initialization or an “odometry backbone” to classify inlier and outlier loop closures. In the
multi-robot case, these assumptions do not always hold. This
paper presents an algorithm called Pairwise Consistency Maximization (PCM) that estimates the largest pairwise internally
consistent set of measurements. Finding the largest pairwise
internally consistent set can be transformed into an instance
of the maximum clique problem from graph theory, and by
leveraging the associated literature it can be solved in realtime. This paper evaluates how well PCM approximates the
combinatorial gold standard using simulated data. It also
evaluates the performance of PCM on synthetic and real-world
data sets in comparison with DCS, SCGP, and RANSAC, and
shows that PCM significantly outperforms these methods.