Maximizing Algebraic Connectivity for Efficient Graph Sparsification in Pose-Graph SLAM

The core message of this paper is that maximizing the algebraic connectivity (Fiedler value) of a pose-graph SLAM measurement graph is an effective approach for producing sparse subgraphs that retain the accuracy of maximum-likelihood estimators applied to the original, dense graph.