Zhang, L., Chen, Z., Cui, W., Li, B., Chen, C., Cao, Z., & Gao, K. (2020). WiFi-Based Indoor Robot Positioning Using Deep Fuzzy Forests. IEEE Internet of Things Journal, 7(11), 10773–10781. doi:10.1109/jiot.2020.2986685
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.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the National Research Foundation, Singapore - -
Grant Reference no. : NRF-RSS2016- 004
This research / project is supported by the Agency for Science, Technology and Research - RIE2020 IAF-PP - Industrial Internet of Things Research Program
Grant Reference no. : A1788a0023
This work was supported by the National Natural Science Foundation of
China (61903231,61803104), Shandong Province Natural Science Foundation (ZR2018PF011).