WiFi-based indoor robot positioning using deep fuzzy forests

Publication Type

Journal Article

Publication Date

4-2020

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 nonvisual 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.

Keywords

wireless fidelity, forestry, databases, visualization, mobile robots, neural networks, deep fuzzy forests, indoor robot positioning, WiFi

Discipline

Computer Engineering

Publication

IEEE Internet of Things Journal

Volume

7

Issue

11

First Page

10773

Last Page

10781

ISSN

2327-4662

Identifier

10.1109/JIOT.2020.2986685

Publisher

Institute of Electrical and Electronics Engineers

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/JIOT.2020.2986685

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