Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/96575
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dc.contributor.authorCarlevaris-Bianco, N.-
dc.contributor.authorKaess, M.-
dc.contributor.authorEustice, R.-
dc.date.issued2014-
dc.identifier.citationIEEE Transactions on Robotics, 2014; 30(6):1371-1385-
dc.identifier.issn1552-3098-
dc.identifier.issn1941-0468-
dc.identifier.urihttp://hdl.handle.net/2440/96575-
dc.description.abstractThis paper reports on a generic factor-based method for node removal in factor-graph simultaneous localization and mapping (SLAM), which we call generic linear constraints (GLCs). The need for a generic node removal tool is motivated by long-term SLAM applications, whereby nodes are removed in order to control the computational cost of graph optimization. GLC is able to produce a new set of linearized factors over the elimination clique that can represent either the true marginalization (i.e., dense GLC) or a sparse approximation of the true marginalization using a Chow-Liu tree (i.e., sparse GLC). The proposed algorithm improves upon commonly used methods in two key ways: First, it is not limited to graphs with strictly full-state relative-pose factors and works equally well with other low-rank factors, such as those produced by monocular vision. Second, the new factors are produced in such a way that accounts for measurement correlation, which is a problem encountered in other methods that rely strictly upon pairwise measurement composition. We evaluate the proposed method over multiple real-world SLAM graphs and show that it outperforms other recently proposed methods in terms of Kullback–Leibler divergence. Additionally, we experimentally demonstrate that the proposed GLC method provides a principled and flexible tool to control the computational complexity of long-term graph SLAM, with results shown for 34.9 h of real-world indoor–outdoor data covering 147.4 km collected over 27 mapping sessions spanning a period of 15 months.-
dc.description.statementofresponsibilityNicholas Carlevaris-Bianco, Michael Kaess, and Ryan M. Eustice-
dc.language.isoen-
dc.publisherIEEE-
dc.rights© 2014 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.-
dc.subjectFactor-graphs; long-term autonomy; marginalization; mobile robotics; simultaneous localization and mapping (SLAM)-
dc.titleGeneric node removal for factor-graph SLAM-
dc.typeJournal article-
dc.identifier.doi10.1109/TRO.2014.2347571-
pubs.publication-statusPublished-
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