(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.