Title:
iSAM: Fast Incremental Smoothing and Mapping with Efficient Data Association
iSAM: Fast Incremental Smoothing and Mapping with Efficient Data Association
Author(s)
Kaess, Michael
Ranganathan, Ananth
Dellaert, Frank
Ranganathan, Ananth
Dellaert, Frank
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Abstract
We introduce incremental smoothing and mapping
(iSAM), a novel approach to the problem of simultaneous
localization and mapping (SLAM) that addresses the data association
problem and allows real-time application in large-scale
environments. We employ smoothing to obtain the complete
trajectory and map without the need for any approximations,
exploiting the natural sparsity of the smoothing information
matrix. A QR-factorization of this information matrix is at
the heart of our approach. It provides efficient access to the
exact covariances as well as to conservative estimates that
are used for online data association. It also allows recovery
of the exact trajectory and map at any given time by backsubstitution.
Instead of refactoring in each step, we update
the QR-factorization whenever a new measurement arrives.
We analyze the effect of loops, and show how our approach
extends to the non-linear case. Finally, we provide experimental
validation of the overall non-linear algorithm based on the
standard Victoria Park data set with unknown correspondences.
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Date Issued
2007-04
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