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Generic Factor-Based Node Marginalization and Edge Sparsification for Pose-Graph SLAM
| Citations: | 11 - 6 self |
BibTeX
@MISC{Carlevaris-bianco_genericfactor-based,
author = {Nicholas Carlevaris-bianco and Ryan M. Eustice},
title = {Generic Factor-Based Node Marginalization and Edge Sparsification for Pose-Graph SLAM},
year = {}
}
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Abstract
Abstract—This paper reports on a factor-based method for node marginalization in simultaneous localization and mapping (SLAM) pose-graphs. Node marginalization in a pose-graph induces fill-in and leads to computational challenges in performing inference. The proposed method is able to produce a new set of constraints over the elimination clique that can represent either the true marginalization, or a sparse approximation of the true marginalization using a Chow-Liu tree. The proposed algorithm improves upon existing methods in two key ways: First, it is not limited to strictly full-state relative-pose constraints and works equally well with other low-rank constraints such as those produced by monocular vision. Second, the new factors are produced in a way that accounts for measurement correlation, a problem ignored in other methods that rely upon measurement composition. We evaluate the proposed method over several realworld SLAM graphs and show that it outperforms other stateof-the-art methods in terms of Kullback-Leibler divergence. I.
Keyphrases
pose-graph slam generic factor-based node marginalization edge sparsification true marginalization node marginalization monocular vision new factor sparse approximation computational challenge several realworld slam graph full-state relative-pose constraint key way pose-graph induces stateof-the-art method elimination clique low-rank constraint measurement composition simultaneous localization chow-liu tree measurement correlation factor-based method kullback-leibler divergence new set
