WiFi signal strength-based robot indoor localization

Yuxiang Sun, Ming Liu, Max Q.H. Meng

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

54 Citations (Scopus)

Abstract

Due 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.

Original languageEnglish
Title of host publication2014 IEEE International Conference on Information and Automation, ICIA 2014
PublisherIEEE
Pages250-256
Number of pages7
ISBN (Electronic)9781479941001
DOIs
Publication statusPublished - 21 Oct 2014
Event2014 IEEE International Conference on Information and Automation, ICIA 2014 - Hailar, Hulunbuir, China
Duration: 28 Jul 201430 Jul 2014

Publication series

Name2014 IEEE International Conference on Information and Automation, ICIA 2014

Conference

Conference2014 IEEE International Conference on Information and Automation, ICIA 2014
Country/TerritoryChina
CityHailar, Hulunbuir
Period28/07/1430/07/14

ASJC Scopus subject areas

  • Modelling and Simulation

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