A hierarchical vision-based localization of rotor unmanned aerial vehicles for autonomous landing

Haiwen Yuan, Changshi Xiao, Supu Xiu, Wenqiang Zhan, Zhenyi Ye, Fan Zhang, Chunhui Zhou, Yuanqiao Wen and Qiliang Li

International Journal of Distributed Sensor Networks, 2018, vol. 14, issue 9, 1550147718800655

Abstract: The vision-based localization of rotor unmanned aerial vehicles for autonomous landing is challenging because of the limited detection range. In this article, to extend the vision detection and measurement range, a hierarchical vision-based localization method is proposed for unmanned aerial vehicle autonomous landing. In such a hierarchical framework, the landing is defined into three phases: “Approaching,†“Adjustment,†and “Touchdown,†in which visual artificial features at different scales can be detected from the designed object pattern for unmanned aerial vehicle pose recovery. The corresponding feature detection and pose estimation algorithms are also presented. In the end, typical simulation and field experiments have been carried out to illustrate the proposed method. The results show that our hierarchical vision-based localization has the ability to a consecutive unmanned aerial vehicle localization in a wider working range from far to near, which is significant for autonomous landing.

Keywords: Hierarchical; vision; localization; unmanned aerial vehicle; landing (search for similar items in EconPapers)
Date: 2018
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/1550147718800655 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:14:y:2018:i:9:p:1550147718800655

DOI: 10.1177/1550147718800655

Access Statistics for this article

More articles in International Journal of Distributed Sensor Networks
Bibliographic data for series maintained by SAGE Publications ().