D. Fox, W. Burgard and S. Thrun
Markov Localization for Mobile Robots in Dynamic Environments
Journal of Artificial Intelligence Research
Abstract
Localization, that is the estimation of a robot's
location from sensor data, is a fundamental problem in mobile
robotics. This papers presents a version of Markov localization which
provides accurate position estimates and which is tailored towards
dynamic environments. The key idea of Markov localization is to
maintain a probability density over the space of all locations of a
robot in its environment. Our approach represents this space
metrically, using a fine-grained grid to approximate densities. It is
able to globally localize the robot from scratch and to recover from
localization failures. It is robust to approximate models of the
environment (such as occupancy grid maps) and noisy sensors (such as
ultrasound sensors). Our approach also includes a filtering technique
which allows a mobile robot to reliably estimate its position even in
densely populated environments in which crowds of people block the
robot's sensors for extended periods of time. The method described
here has been implemented and tested in several real-world
applications of mobile robots, including the deployments of two mobile
robots as interactive museum tour-guides.
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Bibtex
@Article{Fox99Mar,
AUTHOR
= {Fox, D. and Burgard, W. and Thrun, S.},
TITLE
= {Markov Localization for Mobile Robots in Dynamic Environments},
JOURNAL =
{Journal of Artificial Intelligence Research},
VOLUME
= {11},
YEAR
= {1999}
}
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