Hashem Tamimi and Andreas Zell
Global Robot Localization using Iterative Scale Invariant Feature Transform
In Proc. of the 36th International Symposium on Robotics (ISR), Tokyo, Japan, November 2005.
Abstract
We propose an approach that can reduce the feature
extraction time of the Scale Invariant Feature Transform
(SIFT). The main idea is to search for the keypoints around
a set of randomly generated particles rather than to perform
exhaustive search in the whole difference of Gaussian
pyramid. The proposed approach makes it possible to de-
fine the required number of keypoints in advance. A relation
between the number of required features and the feature extraction
time can be drawn. The matching time of two sets
of keypoints is consequently minimized. To demonstrate its
performance, the approach is applied to the robot global localization
problem. The results are successful, with much
less keypoints and less computation time than the original
SIFT.
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Bibtex
@INPROCEEDINGS{Tamimi05-isr1,
AUTHOR = "Hashem {Tamimi} and Andreas {Zell}",
TITLE = "Global Robot Localization using Iterative Scale Invariant Feature Transform",
BOOKTITLE = "In Proc. of the 36th International Symposium on Robotics (ISR)",
ADDRESS ="Tokyo, Japan",
YEAR = "2005"
}