Author: Belmonte, Lidia M.; Morales, Rafael; Fernández-Caballero, Antonio. 2019.
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Abstract
Personal assistant robots provide novel technological solutions in order to monitor people&rsquos activities, helping them in their daily lives. In this sense, unmanned aerial vehicles (UAVs) can also bring forward a present and future model of assistant robots. To develop aerial assistants, it is necessary to address the issue of autonomous navigation based on visual cues. Indeed, navigating autonomously is still a challenge in which computer vision technologies tend to play an outstanding role. Thus, the design of vision systems and algorithms for autonomous UAV navigation and flight control has become a prominent research field in the last few years. In this paper, a systematic mapping study is carried out in order to obtain a general view of this subject. The study provides an extensive analysis of papers that address computer vision as regards the following autonomous UAV vision-based tasks: (1) navigation, (2) control, (3) tracking or guidance, and (4) sense-and-avoid. The works considered in the mapping study&mdasha total of 144 papers from an initial set of 2081&mdashhave been classified under the four categories above. Moreover, type of UAV, features of the vision systems employed and validation procedures are also analyzed. The results obtained make it possible to draw conclusions about the research focuses, which UAV platforms are mostly used in each category, which vision systems are most frequently employed, and which types of tests are usually performed to validate the proposed solutions. The results of this systematic mapping study demonstrate the scientific community&rsquos growing interest in the development of vision-based solutions for autonomous UAVs. Moreover, they will make it possible to study the feasibility and characteristics of future UAVs taking the role of personal assistants.Cite this article
Belmonte LM, Morales R, Fernández-Caballero A. Computer Vision in Autonomous Unmanned Aerial Vehicles—A Systematic Mapping Study. Applied Sciences. 2019; 9(15):3196.https://doi.org/10.3390/app9153196 