W. Burgard, D. Fox, and D. Hennig
Fast Grid-based Position Tracking for Mobile Robots
Proc. of the 21th German Conference on Artificial Intelligence (KI-97)
Abstract
One of the fundamental problems in the field of mobile
robotics is the estimation of the robot's position in the environment.
Position probability grids have been proven to be a robust technique
for the estimation of the absolute position of a mobile robot. In
this paper we describe an application of position probability grids to
position tracking. Given a starting position our approach keeps track
of the robot's current position by matching sensor readings against a
metric model of the environment. The method is designed to work with
noisy sensors and approximative models of the environment.
Furthermore, it is able to integrate sensor readings of different
types of sensors over time. By using raw sensor data, the method
exploits arbitrary features of the environment and, in contrast to
many other approaches, is not restricted to a fixed set of predefined
features such as doors, openings or corridor junction types. An
adaptable sensor model allows a fast integration of new sensings. The
results described in this paper illustrate the robustness of our
method in the presence of sensor noise and errors in the environmental
model.
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Bibtex
@INPROCEEDINGS{Bur97Fas,
AUTHOR
= {Burgard, W. and Fox, D. and Hennig, D},
TITLE
= {Fast Grid-Based Position Tracking for Mobile Robots},
BOOKTITLE = {Proc.~of the 21th German Conference on Artificial Intelligence, Germany},
YEAR
= {1997}
}
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