Paper Info
Title
Infrastructure-Free Global Localization In Repetitive Environments: An Overview
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.
Year
DOI
Venue
2020
10.1109/IECON43393.2020.9255046
IECON 2020: THE 46TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY
DocType
ISSN
Citations 
Conference
1553-572X
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Wu Zhenyu113.39
Jun Zhang200.34
Yufeng Yue385.73
Mingxing Wen425.44
Zichen Jiang500.34
Haoyuan Zhang6114.26
Danwei Wang71529175.13