Title:
DDF-SAM 2.0: Consistent Distributed Smoothing and Mapping
DDF-SAM 2.0: Consistent Distributed Smoothing and Mapping
Author(s)
Cunningham, Alexander
Indelman, Vadim
Dellaert, Frank
Indelman, Vadim
Dellaert, Frank
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Abstract
This paper presents an consistent decentralized
data fusion approach for robust multi-robot SLAM in dan-
gerous, unknown environments. The DDF-SAM 2.0 approach
extends our previous work by combining local and neigh-
borhood information in a single, consistent augmented local
map, without the overly conservative approach to avoiding
information double-counting in the previous DDF-SAM algo-
rithm. We introduce the anti-factor as a means to subtract
information in graphical SLAM systems, and illustrate its use
to both replace information in an incremental solver and to
cancel out neighborhood information from shared summarized
maps. This paper presents and compares three summarization
techniques, with two exact approaches and an approximation.
We evaluated the proposed system in a synthetic example
and show the augmented local system and the associated
summarization technique do not double-count information,
while keeping performance tractable.
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Date Issued
2013-05
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Text
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Post-print
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Proceedings