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Survey of computer vision algorithms and applications for unmanned aerial vehicles.

Authors :
Al-Kaff, Abdulla
Martín, David
García, Fernando
Escalera, Arturo de la
María Armingol, José
Source :
Expert Systems with Applications. Feb2018, Vol. 92, p447-463. 17p.
Publication Year :
2018

Abstract

This paper presents a complete review of computer vision algorithms and vision-based intelligent applications, that are developed in the field of the Unmanned Aerial Vehicles (UAVs) in the latest decade. During this time, the evolution of relevant technologies for UAVs; such as component miniaturization, the increase of computational capabilities, and the evolution of computer vision techniques have allowed an important advance in the development of UAVs technologies and applications. Particularly, computer vision technologies integrated in UAVs allow to develop cutting-edge technologies to cope with aerial perception difficulties; such as visual navigation algorithms, obstacle detection and avoidance and aerial decision-making. All these expert technologies have developed a wide spectrum of application for UAVs, beyond the classic military and defense purposes. Unmanned Aerial Vehicles and Computer Vision are common topics in expert systems, so thanks to the recent advances in perception technologies, modern intelligent applications are developed to enhance autonomous UAV positioning, or automatic algorithms to avoid aerial collisions, among others. Then, the presented survey is based on artificial perception applications that represent important advances in the latest years in the expert system field related to the Unmanned Aerial Vehicles. In this paper, the most significant advances in this field are presented, able to solve fundamental technical limitations; such as visual odometry, obstacle detection, mapping and localization, et cetera. Besides, they have been analyzed based on their capabilities and potential utility. Moreover, the applications and UAVs are divided and categorized according to different criteria. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
09574174
Volume :
92
Database :
Academic Search Index
Journal :
Expert Systems with Applications
Publication Type :
Academic Journal
Accession number :
125705551
Full Text :
https://doi.org/10.1016/j.eswa.2017.09.033