Title:
Inference In The Space Of Topological Maps: An MCMC-based Approach
Inference In The Space Of Topological Maps: An MCMC-based Approach
Author(s)
Ranganathan, Ananth
Dellaert, Frank
Dellaert, Frank
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Abstract
While probabilistic techniques have been considered
extensively in the context of metric maps, no general
purpose probabilistic methods exist for topological maps.
We present the concept of Probabilistic Topological Maps
(PTMs), a sample-based representation that approximates
the posterior distribution over topologies given the available
sensor measurements. The PTM is obtained through the
use of MCMC-based Bayesian inference over the space of
all possible topologies. It is shown that the space of all
topologies is equivalent to the space of set partitions of all
available measurements. While the space of possible topologies
is intractably large, our use of Markov chain Monte Carlo
sampling to infer the approximate histograms overcomes the
combinatorial nature of this space and provides a general
solution to the correspondence problem in the context of
topological mapping. We present experimental results that
validate our technique and generate good maps even when
using only odometry as the sensor measurements.
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Date Issued
2004-09
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