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Self-Localization in Highly Dynamic Environments Based on Dual-Channel Unscented Particle Filter

Published online by Cambridge University Press:  19 November 2020

Chen Hao Open the ORCID record for Chen Hao [Opens in a new window] ,
Liu Chengju  and
Chen Qijun
Chen Hao*
Affiliation:
School of Electronics and Information Engineering, Tongji University, Shanghai, China E-mails: liuchengju@tongji.edu.cn, qjchen@tongji.edu.cn
Liu Chengju
Affiliation:
School of Electronics and Information Engineering, Tongji University, Shanghai, China E-mails: liuchengju@tongji.edu.cn, qjchen@tongji.edu.cn
Chen Qijun
Affiliation:
School of Electronics and Information Engineering, Tongji University, Shanghai, China E-mails: liuchengju@tongji.edu.cn, qjchen@tongji.edu.cn
*
*Corresponding author. E-mail: hao.chen@tongji.edu.cn
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Summary

Self-localization in highly dynamic environments is still a challenging problem for humanoid robots with limited computation resource. In this paper, we propose a dual-channel unscented particle filter (DC-UPF)-based localization method to address it. A key novelty of this approach is that it employs a dual-channel switch mechanism in measurement updating procedure of particle filter, solving for sparse vision feature in motion, and it leverages data from a camera, a walking odometer, and an inertial measurement unit. Extensive experiments with an NAO robot demonstrate that DC-UPF outperforms UPF and Monte–Carlo localization with regard to accuracy.

Keywords

Self-localizationHumanoid robotsDynamic environment(Dual-channel unscented particle filter (DC-UPF)Data fusion
Type
Article
Information
Robotica , Volume 39 , Issue 7 , July 2021 , pp. 1216 - 1229
Copyright
© The Author(s), 2020. Published by Cambridge University Press

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