(Georgia Institute of Technology, 2013-05)
Cunningham, Alexander; Indelman, Vadim; Dellaert, Frank
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.
(Georgia Institute of Technology, 2010)
Cunningham, Alexander; Paluri, Manohar; Dellaert, Frank
We address the problem of multi-robot distributed
SLAM with an extended Smoothing and Mapping
(SAM) approach to implement Decentralized Data Fusion
(DDF). We present DDF-SAM, a novel method for efficiently
and robustly distributing map information across
a team of robots, to achieve scalability in computational
cost and in communication bandwidth and robustness to
node failure and to changes in network topology. DDF-SAM
consists of three modules: (1) a local optimization
module to execute single-robot SAM and condense the
local graph; (2) a communication module to collect and
propagate condensed local graphs to other robots, and
(3) a neighborhood graph optimizer module to combine
local graphs into maps describing the neighborhood of a
robot. We demonstrate scalability and robustness through a
simulated example, in which inference is consistently faster
than a comparable naive approach.