(Georgia Institute of Technology, 2003)
Alegre, Fernando; Dellaert, Frank; Martinson, Eric Beowulf
We investigate Intrinsic Localization and Mapping
(ILM) for teams of mobile robots, a multi-robot variant of
SLAM where the robots themselves are used as landmarks.
We develop what is essentially a straightforward application
of Bayesian estimation to the problem, and present two complimentary
views on the associated optimization problem that
provide insight into the problem and allows one to devise initialization
strategies, indispensable in practice. We also provide
a discussion of the degrees of freedom and ambiguities
in the solution. Finally, we introduce two applications of ILM
that bring out its potential: Diffusion Mapping and Marco Polo
localization.
(Georgia Institute of Technology, 2003)
Balch, Tucker; Dellaert, Frank; Khan, Zia
We describe a multiple hypothesis particle filter
for tracking targets that will be influenced by the proximity
and/or behavior of other targets. Our contribution is to show
how a Markov random field motion prior, built on the fly
at each time step, can model these interactions to enable
more accurate tracking. We present results for a social insect
tracking application, where we model the domain knowledge
that two targets cannot occupy the same space, and targets
will actively avoid collisions. We show that using this model
improves track quality and efficiency. Unfortunately, the
joint particle tracker we propose suffers from exponential
complexity in the number of tracked targets. An approximation
to the joint filter, however, consisting of multiple
nearly independent particle filters can provide similar track
quality at substantially lower computational cost.
(Georgia Institute of Technology, 2003)
Dellaert, Frank; Martinson, Eric Beowulf
We introduce the Marco Polo Localization approach, where
we apply sound as a tool for gathering range measurements
between robots, and use those to solve a range-only Simultaneous
Localization and Mapping problem. Range is calculated
by correlating two recordings of the same sound, recorded on
a pair of robots, after which the resulting time delay estimate is
converted to a range measurement. The algorithmic approach
we use is a straightforward application of the Bayesian estimation
framework. We also present two complementary views on
the associated optimization problem that provide insight into
the problem and allows one to devise initialization strategies,
indispensable in a range-only scenario. We illustrate the approach
with both simulated and experimental results.