Title:
Data Driven MCMC for Appearance-Based Topological Mapping
Data Driven MCMC for Appearance-Based Topological Mapping
Author(s)
Ranganathan, Ananth
Dellaert, Frank
Dellaert, Frank
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Abstract
Probabilistic techniques have become the mainstay of robotic mapping, particularly
for generating metric maps. In previous work, we have presented a hitherto
nonexistent general purpose probabilistic framework for dealing with topological
mapping. This involves the creation of Probabilistic Topological Maps
(PTMs), a sample-based representation that approximates the posterior distribution
over topologies given available sensor measurements. The PTM is inferred using
Markov Chain Monte Carlo (MCMC) that overcomes the combinatorial nature of
the problem. In this paper, we address the problem of integrating appearance measurements
into the PTM framework. Specifically, we consider appearance measurements
in the form of panoramic images obtained from a camera rig mounted
on a robot. We also propose improvements to the efficiency of the MCMC algorithm
through the use of an intelligent data-driven proposal distribution. We
present experiments that illustrate the robustness and wide applicability of our algorithm.
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Date Issued
2005
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Proceedings