Title:
The Bayes Tree: Enabling Incremental Reordering and Fluid Relinearization for Online Mapping
The Bayes Tree: Enabling Incremental Reordering and Fluid Relinearization for Online Mapping
Author(s)
Kaess, Michael
Ila, Viorela
Roberts, Richard
Dellaert, Frank
Ila, Viorela
Roberts, Richard
Dellaert, Frank
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Abstract
In this paper we present a novel data structure, the Bayes tree, which exploits
the connections between graphical model inference and sparse linear algebra.
The proposed data structure provides a new perspective on an entire class of
simultaneous localization and mapping (SLAM) algorithms. Similar to a junction
tree, a Bayes tree encodes a factored probability density, but unlike the junction
tree it is directed and maps more naturally to the square root information matrix of
the SLAM problem. This makes it eminently suited to encode the sparse nature of
the problem, especially in a smoothing and mapping (SAM) context. The inherent
sparsity of SAM has already been exploited in the literature to produce efficient
solutions in both batch and online mapping. The graphical model perspective
allows us to develop a novel incremental algorithm that seamlessly incorporates
reordering and relinearization. This obviates the need for expensive periodic batch
operations from previous approaches, which negatively affect the performance
and detract from the intended online nature of the algorithm. The new method
is evaluated using simulated and real-world datasets in both landmark and pose
SLAM settings.
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Date Issued
2010-01-29
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Text
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Technical Report