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WiFi SLAM algorithms: an experimental comparison

Published online by Cambridge University Press:  18 July 2014

F. Herranz*
Affiliation:
Department of Electronics, Polytechnic School, University Campus, University of Alcalá, 28871 Alcalá de Henares, Madrid, Spain
A. Llamazares
Affiliation:
Department of Electronics, Polytechnic School, University Campus, University of Alcalá, 28871 Alcalá de Henares, Madrid, Spain
E. Molinos
Affiliation:
Department of Electronics, Polytechnic School, University Campus, University of Alcalá, 28871 Alcalá de Henares, Madrid, Spain
M. Ocaña
Affiliation:
Department of Electronics, Polytechnic School, University Campus, University of Alcalá, 28871 Alcalá de Henares, Madrid, Spain
M. A. Sotelo
Affiliation:
Computer Engineering Department, Polytechnic School, University Campus, University of Alcalá, 28871 Alcalá de Henares, Madrid, Spain
*
*Corresponding author. E-mail: fernando.herranz@depeca.uah.es

Summary

Localization and mapping in indoor environments, such as airports and hospitals, are key tasks for almost every robotic platform. Some researchers suggest the use of Range-Only (RO) sensors based on WiFi (Wireless Fidelity) technology with SLAM (Simultaneous Localization And Mapping) techniques to solve both problems. The current state of the art in RO SLAM is mainly focused on the filtering approach, while the study of smoothing approaches with RO sensors is quite incomplete. This paper presents a comparison between filtering algorithms, such as EKF and FastSLAM, and a smoothing algorithm, the SAM (Smoothing And Mapping). Experimental results are obtained in indoor environments using WiFi sensors. The results demonstrate the feasibility of the smoothing approach using WiFi sensors in an indoor environment.

Type
Articles
Copyright
Copyright © Cambridge University Press 2014 

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