iSAM2: Incremental Smoothing and Mapping Using the Bayes Tree

TitleiSAM2: Incremental Smoothing and Mapping Using the Bayes Tree
Publication TypeJournal Article
Year of Publication2012
AuthorsKaess M, Johnnsson H, Roberts R, Ila V, Leonard JJ, Dellaert F
JournalThe International Journal of Robotics Research
Volume31
Pagination217-236
Abstract

We present a novel data structure, the Bayes tree, that provides an algorithmic foundation enabling a better understanding of existing graphical model inference algorithms and their connection to sparse matrix factorization methods Similar to a clique tree, a Bayes tree encodes a factored probability density, but unlike the clique tree it is directed and maps more naturally to the square root information matrix of the simultaneous localization and mapping (SLAM) problem In this paper, we highlight three insights provided by our new data structure First, the Bayes tree provides a better understanding of the matrix factorization in terms of probability densities Second, we show how the fairly abstract updates to a matrix factorization translate to a simple editing of the Bayes tree and its conditional densities Third, we apply the Bayes tree to obtain a completely novel algorithm for sparse nonlinear incremental optimization, named iSAM2, which achieves improvements in efficiency through incremental variable re-ordering and fluid relinearization, eliminating the need for periodic batch steps We analyze various properties of iSAM2 in detail, and show on a range of real and simulated datasets that our algorithm compares favorably with other recent mapping algorithms in both quality and efficiency

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