Map-based Priors for Localization
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Abstract
Localization from sensor measurements is a
fundamental task for navigation. Particle filters are among
the most promising candidates to provide a robust and realtime
solution to the localization problem. They instantiate
the localization problem as a Bayesian filtering problem and
approximate the posterior density over location by a weighted
sample set. In this paper, we introduce map-based priors for
localization, using the semantic information available in maps
to bias the motion model toward areas of higher probability.
We show that such priors, under a particular assumption
, can easily be incorporated in the particle filter by means
of a pseudo likelihood. The resulting filter is more reliable
and more accurate. We show experimental results on a GPS-based
outdoor people tracker that illustrate the approach and
highlight its potential.
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2004-09
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