Title:
iSAM2: Incremental Smoothing and Mapping with Fluid Relinearization and Incremental Variable Reordering
iSAM2: Incremental Smoothing and Mapping with Fluid Relinearization and Incremental Variable Reordering
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Author(s)
Kaess, Michael
Johannsson, Hordur
Roberts, Richard
Ila, Viorela
Leonard, John
Dellaert, Frank
Johannsson, Hordur
Roberts, Richard
Ila, Viorela
Leonard, John
Dellaert, Frank
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Abstract
We present iSAM2, a fully incremental, graphbased
version of incremental smoothing and mapping (iSAM).
iSAM2 is based on a novel graphical model-based interpretation
of incremental sparse matrix factorization methods, afforded by
the recently introduced Bayes tree data structure. The original
iSAM algorithm incrementally maintains the square root
information matrix by applying matrix factorization updates.
We analyze the matrix updates as simple editing operations
on the Bayes tree and the conditional densities represented by
its cliques. Based on that insight, we present a new method
to incrementally change the variable ordering which has a
large effect on efficiency. The efficiency and accuracy of the
new method is based on fluid relinearization, the concept of
selectively relinearizing variables as needed. This allows us
to obtain a fully incremental algorithm without any need for
periodic batch steps. We analyze the properties of the resulting
algorithm in detail, and show on various real and simulated
datasets that the iSAM2 algorithm compares favorably with
other recent mapping algorithms in both quality and efficiency.
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Date Issued
2011
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Post-print