# Computer Vision in Autonomous Unmanned Aerial Vehicles--A Systematic Mapping Study
## 1 Introduction  
The use of unmanned aerial vehicles (UAVs) has significantly increased in recent years. These aircraft are mainly characterized by the fact that they allow access to remote places without the direct intervention of a human operator aboard. These places are generally difficult to access and/or have unfavorable conditions. UAVs' abilities are permitting their use in manifold applications, such as remote sensing, support in emergency situations, inspection of infrastructures, logistics systems, professional photography and video, and precision agriculture spray systems, among others [1]. An emerging domain is flying assistance robotics, where UAVs come through with a present and future model of fully autonomous personal monitoring capacities. Some examples are Aire, a self-flying robotic assistant for the home [2], Fleye, a personal flying robot [3], and CIMON and Astrobee, flying assistant robots in the space station [4, 5]. Personal assistant robots are principally based on monitoring people's activities in order to provide them help and support in daily activities. Our current research interest is framed in this field. One of our major objectives is to design an autonomous aerial vehicle to assist dependent people [6; 7]. In this sense, it is necessary that a vision system captures images of the dependent person, which are analyzed in order to determine the assistance required at each moment. Therefore, it is necessary to address the problem of autonomous navigation in both indoor and outdoor environments. In other words, the UAV must perform flights in a completely autonomous manner. At this point, and given the need to have a vision system for monitoring the assisted person, both issues may be intertwined. In this respect, a series of complex operations must still be solved in order to achieve fully autonomous UAVs. Full autonomous navigation means flying in an environment without even requiring the control of an operator on land. In this respect, it should be noted that a UAV requires sensors to (a) measure the aircraft's state, (b) sense the environment, (c) detect landmarks and targets (in tracking missions), and even (d) detect both static and dynamic obstacles, among others, if it is to fly autonomously. Moreover, it is necessary to integrate this information into a control system so as to ensure that the movement of the aircraft is accurate and safe. Vision systems play an outstanding role as regards performing all these complex and interrelated tasks, since images captured by a vision camera contain a vast amount of data concerning the flight environment. This information is then extracted and analyzed by using computer vision algorithms to obtain useful information for navigation and flight control. As a result, the amount of works focused on the development of solutions based on computer vision for autonomous UAVs has grown notably in the last few years. We therefore present a systematic mapping of literature in order to summarize and analyze the research carried out on this topic. A systematic mapping is a method that is employed to review, classify and structure documents focused on a specific research topic. Systematic mapping studies were initially used principally in the field of medical research, but they are now also being applied to other areas related to engineering and new technologies, such as web development [8], mobile devices [9], social-technical congruence [10], and unmanned aerial systems in smart cities [11]. With regard to the topic of computer vision systems in mobile robots, reviews and meta-analyses have been presented for both ground [12] and aerial vehicles [13; 14; 15]. However, a systematic mapping study that provides an objective procedure with which to identify the nature and extent of the research on this topic has not been conducted to date. This is, therefore, to the best of our knowledge, the first systematic mapping study focused on how computer vision is being used in autonomous flying robots.