| Volume 26 Issue 9 - Publication Date: 1 September 2007 |
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| Randomized Algorithms
for Minimum Distance
Localization |
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| M. Rao, G. Dudek, and S. Whitesides Department of Computer Science
McGill University, Montréal
Canada H3A 2A7 |
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| The problem of minimum distance localization in environments that
may contain self-similarities is addressed. A mobile robot is placed at
an unknown location inside a 2D self-similar polygonal environment
P. The robot has a map of P and can compute visibility data through
sensing. However, the self-similarities in the environment mean that
the same visibility data may correspond to several different locations.
The goal, therefore, is to determine the robot’s true initial location
while minimizing the distance traveled by the robot. Two randomized
approximation algorithms are presented that solve minimum distance
localization. The performance of the proposed algorithms is evaluated
empirically. |
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