Title:
Eliminating Conditionally Independent Sets in Factor Graphs: A Unifying Perspective based on Smart Factors
Eliminating Conditionally Independent Sets in Factor Graphs: A Unifying Perspective based on Smart Factors
Author(s)
Carlone, Luca
Kira, Zsolt
Beall, Chris
Indelman, Vadim
Dellaert, Frank
Kira, Zsolt
Beall, Chris
Indelman, Vadim
Dellaert, Frank
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Abstract
Factor graphs are a general estimation framework
that has been widely used in computer vision and robotics. In
several classes of problems a natural partition arises among
variables involved in the estimation. A subset of the variables
are actually of interest for the user: we call those
target
variables. The remaining variables are essential for the formulation of the optimization problem underlying maximum a
posteriori (MAP) estimation; however these variables, that we
call
support
variables, are not strictly required as output of the
estimation problem. In this paper, we propose a systematic way
to abstract support variables, defining optimization problems
that are only defined over the set of target variables. This
abstraction naturally leads to the definition of
smart factors, which correspond to constraints among target variables. We
show that this perspective unifies the treatment of heterogeneous problems, ranging from structureless bundle adjustment
to robust estimation in SLAM. Moreover, it enables to exploit
the underlying structure of the optimization problem and the
treatment of degenerate instances, enhancing both computational efficiency and robustness.
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Date Issued
2014
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Text
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Proceedings