WiFi signal strength-based robot indoor localization
Refereed conference paper presented and published in conference proceedings

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AbstractDue to the unavailable GPS signals in indoor environments, indoor localization has become an increasingly heated research topic in recent years. Researchers in robotics community have tried many approaches, but this is still an unsolved problem considering the balance between performance and cost. The widely deployed low-cost WiFi infrastructure provides a great opportunity for indoor localization. In this paper, we develop a system for WiFi signal strength-based indoor localization and implement two approaches. The first is improved KNN algorithm-based fingerprint matching method, and the other is the Gaussian Process Regression (GPR) with Bayes Filter approach. We conduct experiments to compare the improved KNN algorithm with the classical KNN algorithm and evaluate the localization performance of the GPR with Bayes Filter approach. The experiment results show that the improved KNN algorithm can bring enhancement for the fingerprint matching method compared with the classical KNN algorithm. In addition, the GPR with Bayes Filter approach can provide about 2m localization accuracy for our test environment.
All Author(s) ListSun Y., Liu M., Meng M.Q.-H.
Name of Conference2014 IEEE International Conference on Information and Automation, ICIA 2014
Start Date of Conference28/07/2014
End Date of Conference30/07/2014
Place of ConferenceHailar, Hulunbuir
Country/Region of ConferenceChina
Detailed descriptionorganized by IEEE,
Pages250 - 256
LanguagesEnglish-United Kingdom

Last updated on 2020-30-11 at 00:27