Journal
IEEE INTERNET OF THINGS JOURNAL
Volume 7, Issue 11, Pages 10773-10781Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2020.2986685
Keywords
Wireless fidelity; Forestry; Databases; Visualization; Mobile robots; Neural networks; Deep fuzzy forests; indoor robot positioning; WiFi
Categories
Funding
- National Natural Science Foundation of China [61903231, 61803104]
- Shandong Province Natural Science Foundation [ZR2018PF011]
- Singapore National Research Foundation [NRF-RSS2016-004]
- A*STAR Industrial Internet of Things Research Program through the RIE2020 IAF-PP [A1788a0023]
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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.
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