Title:
Support-Theoretic Subgraph Preconditioners for Large-Scale SLAM
Support-Theoretic Subgraph Preconditioners for Large-Scale SLAM
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Author(s)
Jian, Yong-Dian
Balcan, Doru
Panageas, Ioannis
Tetali, Prasad
Dellaert, Frank
Balcan, Doru
Panageas, Ioannis
Tetali, Prasad
Dellaert, Frank
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Abstract
Efficiently solving large-scale sparse linear systems is important for robot mapping and navigation. Recently,
the subgraph-preconditioned conjugate gradient method has
been proposed to combine the advantages of two reigning
paradigms, direct and iterative methods, to improve the efficiency of the solver. Yet the question of how to pick a good subgraph is still open. In this paper, we propose a new metric to
measure the quality of a spanning tree preconditioner based on
support theory. We use this metric to develop an algorithm to
find good subgraph preconditioners and apply them to solve the
SLAM problem. The results show that although the proposed
algorithm is not fast enough, the new metric is effective and
resulting subgraph preconditioners significantly improve the
efficiency of the state-of-the-art solver.
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Date Issued
2013-11
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Post-print
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