TY - GEN
T1 - WiFi signal strength-based robot indoor localization
AU - Sun, Yuxiang
AU - Liu, Ming
AU - Meng, Max Q.H.
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/10/21
Y1 - 2014/10/21
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84914095486&partnerID=8YFLogxK
U2 - 10.1109/ICInfA.2014.6932662
DO - 10.1109/ICInfA.2014.6932662
M3 - Conference article published in proceeding or book
AN - SCOPUS:84914095486
T3 - 2014 IEEE International Conference on Information and Automation, ICIA 2014
SP - 250
EP - 256
BT - 2014 IEEE International Conference on Information and Automation, ICIA 2014
PB - IEEE
T2 - 2014 IEEE International Conference on Information and Automation, ICIA 2014
Y2 - 28 July 2014 through 30 July 2014
ER -