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Title: WiFi-Based Indoor Robot Positioning Using Deep Fuzzy Forests
Authors: ZHANG LE 
Chen Zhenghua
Cui Wei
Li Bing
Chen Cen
Gao Kaizhou
Issue Date: 8-Apr-2020
Publisher: IEEE
Citation: ZHANG LE, Chen Zhenghua, Cui Wei, Li Bing, Chen Cen, CAO ZHIGUANG, Gao Kaizhou (2020-04-08). WiFi-Based Indoor Robot Positioning Using Deep Fuzzy Forests. IEEE Internet of Things Journal. ScholarBank@NUS Repository.
Rights: Attribution-NonCommercial-ShareAlike 4.0 International
Abstract: Addressing the positioning problem of a mobile robot remains challenging to date despite many years of research. Indoor robot positioning strategies developed in the literature either rely on sophisticated computer vision techniques to handle visual inputs or require strong domain knowledge for non-visual sensors. Although some systems have been deployed, the former may be lacking due to the intrinsic limitation of cameras (such as calibration, data association, system initialization, etc.) and the latter usually only works under certain environment layouts and additional equipment. To cope with those issues, we design a lightweight indoor robot positioning system which operates on cost-effective WiFi based Received Signal Strength (RSS) and could be readily pluggable into any existing WiFi network infrastructures. Moreover, a novel deep fuzzy forest is proposed to inherit the merits of decision trees and deep neural networks within an end-to-end trainable architecture. Real-world indoor localization experiments are conducted and results demonstrate the superiority of the proposed method over the existing approaches.
Source Title: IEEE Internet of Things Journal
ISSN: 23274662
DOI: 10.1109/JIOT.2020.2986685
Rights: Attribution-NonCommercial-ShareAlike 4.0 International
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