Spectral Measurement Sparsification for Pose-Graph SLAM
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by
Kevin J. Doherty and David M. Rosen and John J. Leonard
2022
Abstract
Simultaneous localization and mapping (SLAM) is a critical capability in
autonomous navigation, but in order to scale SLAM to the setting of "lifelong"
SLAM, particularly under memory or computation constraints, a robot must be
able to determine what information should be retained and what can safely be
forgotten. In graph-based SLAM, the number of edges (measurements) in a pose
graph determines both the memory requirements of storing a robot's observations
and the computational expense of algorithms deployed for performing state
estimation using those observations; both of which can grow unbounded during
long-term navigation. To address this, we propose a spectral approach for pose
graph sparsification which maximizes the algebraic connectivity of the
sparsified measurement graphs, a key quantity which has been shown to control
the estimation error of pose graph SLAM solutions. Our algorithm, MAC (for
"maximizing algebraic connectivity"), which is based on convex relaxation, is
simple and computationally inexpensive, and admits formal post hoc performance
guarantees on the quality of the solutions it provides. In experiments on
benchmark pose-graph SLAM datasets, we show that our approach quickly produces
high-quality sparsification results which retain the connectivity of the graph
and, in turn, the quality of corresponding SLAM solutions, as compared to a
baseline approach which does not consider graph connectivity.
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