(Georgia Institute of Technology, 2010-12)
Rogers, John G.; Trevor, Alexander J. B.; Nieto-Granda, Carlos; Cunningham, Alexander; Paluri, Manohar; Michael, Nathan; Dellaert, Frank; Christensen, Henrik I.; Kumar, Vijay
This paper will explore the relationship between sensory accuracy and Simultaneous Localization and Mapping (SLAM) performance. As inexpensive robots are developed with commodity components, the relationship between performance level and accuracy will need to be determined. Experiments are presented in this paper which compare various aspects of sensor performance such as maximum range, noise, angular precision, and viewable angle. In addition, mapping results from three popular laser scanners (Hokuyo’s URG and UTM30, as well as SICK’s LMS291) are compared.
(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.