Title:
Loopy SAM
Loopy SAM
Author(s)
Ranganathan, Ananth
Kaess, Michael
Dellaert, Frank
Kaess, Michael
Dellaert, Frank
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Abstract
Smoothing approaches to the Simultaneous Localization
and Mapping (SLAM) problem in robotics
are superior to the more common filtering approaches
in being exact, better equipped to deal
with non-linearities, and computing the entire robot
trajectory. However, while filtering algorithms that
perform map updates in constant time exist, no
analogous smoothing method is available. We aim
to rectify this situation by presenting a smoothing-based
solution to SLAM using Loopy Belief Propagation
(LBP) that can perform the trajectory and
map updates in constant time except when a loop
is closed in the environment. The SLAM problem
is represented as a Gaussian Markov Random
Field (GMRF) over which LBP is performed.
We prove that LBP, in this case, is equivalent to
Gauss-Seidel relaxation of a linear system. The inability
to compute marginal covariances efficiently
in a smoothing algorithm has previously been a
stumbling block to their widespread use. LBP
enables the efficient recovery of the marginal covariances,
albeit approximately, of landmarks and
poses. While the final covariances are overconfident,
the ones obtained from a spanning tree of the
GMRF are conservative, making them useful for
data association. Experiments in simulation and using
real data are presented.
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Date Issued
2007-01
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Text
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Post-print
Proceedings
Proceedings