D. Fox, W. Burgard, and S. Thrun
Markov Localization for Reliable
Robot Navigation and People Detection
Proc. of the Dagstuhl Seminar on Modelling and Planning
for Sensor-Based Intelligent Robot Systems
Abstract
Localization is one of the fundamental problems in mobile robotics. Without
knowledge about their position mobile robots cannot efficiently carry out
their tasks. In this paper we present Markov localization as a technique
for estimating the position of a mobile robot. The key idea of this technique
is to maintain a probability density over the whole state space of the
robot within its environment. This way our technique is able to globally
localize the robot from scratch and even to recover from localization failures,
a property which is essential for truly autonomous robots. The probabilistic
framework makes this approach robust against approximate models of the
environment as well as noisy sensors. Based on a fine-grained, metric discretization
of the state space, Markov localization is able to incorporate raw sensor
readings and does not require predefined landmarks. It also includes a
filtering technique which allows to reliably estimate the position of a
mobile robot even in densely populated environments. We furthermore describe,
how the explicit representation of the density can be exploited in a reactive
collision avoidance system to increase the robustness and reliability of
the robot even in situations in which it is uncertain about its position.
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
@INPROCEEDINGS{,
AUTHOR
= {Fox, D. and Burgard, W. and Thrun, S.},
TITLE
= {Markov Localization for Reliable Robot Navigation and People Detection},
YEAR
= {1999},
SERIES
= {Lecture Notes in Computer Science},
PUBLISHER = {Springer Verlag},
BOOKTITLE = {Proc.~of the Dagstuhl Seminar
on Modelling and
Planning for Sensor-Based Intelligent Robot Systems}
}
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