Conservative edge sparsification for graph SLAM node removal

This paper reports on optimization-based methods for producing a sparse, conservative approximation of the dense potentials induced by node marginalization in simultaneous localization and mapping (SLAM) factor graphs. The proposed methods start with a sparse, but overconfident, Chow-Liu tree approximation of the marginalization potential and then use optimization-based methods to adjust the approximation so that it is conservative subject to minimizing the Kullback-Leibler divergence (KLD) from the true marginalization potential. Results are presented over multiple real-world SLAM graphs and show that the proposed methods enforce a conservative approximation, while achieving low KLD from the true marginalization potential.

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