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Title: Infrastructure-free global localization in repetitive environments : an overview
Authors: Wu, Zhenyu
Zhang, Jun
Yue, Yufeng
Wen, Mingxing
Jiang, Zichen
Zhang, Haoyuan
Wang, Danwei
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2020
Source: Wu, Z., Zhang, J., Yue, Y., Wen, M., Jiang, Z., Zhang, H., & Wang, D. (2020). Infrastructure-free global localization in repetitive environments : an overview. Proceedings of the Annual Conference of the IEEE Industrial Electronics Society (IECON). doi:10.1109/IECON43393.2020.9255046
metadata.dc.contributor.conference: IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society (IECON)
Abstract: Repetitive environment is a challenging scenario for mobile robot global localization due to its highly similar structures and lack of distinctive features. Existing solutions in such environments rely heavily on pre-installed infrastructures, which are neither flexible nor cost-effective. Besides, few of the previous research have been focused on the implementation of infrastructure-free localization approaches in repetitive scenarios. Thus, this paper serves as a survey to investigate the problem of infrastructure-free mobile robot global localization with low-cost and efficient sensors in repetitive environments. Three of the most popular infrastructure-free localization methods, namely LiDAR-based localization (LBL), vision-based localization (VBL), and magnetic field-based localization (MFL), are analyzed and evaluated. Extensive global localization experiments are conducted in real-world repetitive scenarios and the results demonstrate that VBL methods perform slightly better than LBL and MFL methods. The overall evaluations indicate that infrastructure-free global localization in repetitive environment is still a challenging problem which deserves more research efforts to develop new solutions.
DOI: 10.1109/IECON43393.2020.9255046
Schools: School of Electrical and Electronic Engineering 
Research Centres: ST Engineering-NTU Corporate Lab 
Rights: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at:
Fulltext Permission: open
Fulltext Availability: With Fulltext
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