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Discussion and Conclusion
Robot localization in a dynamic indoor environment is a crucial research topic for
indoor service robot. This paper presents a robust feature matching method using
Omni-directional grouping. The main contribution of this research is the method can
work well for the global localization problem in a dynamic environment. Our
experiments show that even 22% of information is not in the pre-stored database; our
method can help a robot find its location at high accuracy.
The method can deal with the problem that a robot's position and direction is
unknown, and the accuracy has improved significantly. Experimental result shows
that our method can improve the localization in both static and dynamic environment.
Our feature matching is motivated by the signature-rs method, and achieves better
performance both in static environment and dynamic environment.
Acknowledgement. This research was supported by the Ministry of Science and
Technology, Taiwan, R.O.C., under contract number MOST103-2632-E-324-001-MY3.
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