Title:
iSAM: Incremental Smoothing and Mapping
iSAM: Incremental Smoothing and Mapping
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Author(s)
Kaess, Michael
Ranganathan, Ananth
Dellaert, Frank
Ranganathan, Ananth
Dellaert, Frank
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Abstract
We present incremental smoothing and mapping
(iSAM), a novel approach to the simultaneous localization and
mapping problem that is based on fast incremental matrix
factorization. iSAM provides an efficient and exact solution by
updating a QR factorization of the naturally sparse smoothing information
matrix, therefore recalculating only the matrix entries
that actually change. iSAM is efficient even for robot trajectories
with many loops as it avoids unnecessary fill-in in the factor
matrix by periodic variable reordering. Also, to enable data
association in real-time, we provide efficient algorithms to access
the estimation uncertainties of interest based on the factored
information matrix. We systematically evaluate the different
components of iSAM as well as the overall algorithm using
various simulated and real-world datasets for both landmark
and pose-only settings.
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Date Issued
2008
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Text
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Post-print