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
forestimating the position of a mobile robot. The key idea of this technique
is to maintain a probability density over the whole state space of therobot
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
aswell as noisy sensors. Based on a fine-grained, metric discretization of
thestate 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
denselypopulated environments. We furthermore describe,how the explicit representation
of the density can be exploited in a reactive collision avoidance system
toincrease the robustness and reliability ofthe robot even in situations
inwhich it is uncertain about its position.The method described here has
beenimplemented and tested in several real-world applications of mobile robots
including the deployments of two mobile robotsas 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 Seminaron Modelling and Planning
for Sensor-Based Intelligent Robot Systems}
}