TY - GEN
T1 - Infrastructure-Free Global Localization in Repetitive Environments
T2 - 46th Annual Conference of the IEEE Industrial Electronics Society, IECON 2020
AU - Wu, Zhenyu
AU - Zhang, Jun
AU - Yue, Yufeng
AU - Wen, Mingxing
AU - Jiang, Zichen
AU - Zhang, Haoyuan
AU - Wang, Danwei
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/10/18
Y1 - 2020/10/18
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85097743347&partnerID=8YFLogxK
U2 - 10.1109/IECON43393.2020.9255046
DO - 10.1109/IECON43393.2020.9255046
M3 - Conference contribution
AN - SCOPUS:85097743347
T3 - IECON Proceedings (Industrial Electronics Conference)
SP - 626
EP - 631
BT - Proceedings - IECON 2020
PB - IEEE Computer Society
Y2 - 19 October 2020 through 21 October 2020
ER -