# Computer Vision in Autonomous Unmanned Aerial Vehicles--A Systematic Mapping Study
## 4 Discussion  
* _Number and Relevance of the Papers:_ (i) The number of research works focused on the use of computer vision for autonomous aerial vehicles has not stopped growing in recent years, which confirms the scientific community's interest in this topic. (ii) More than 86% of the papers analyzed in this study were published in journals indexed in JCR in such outstanding areas as Aerospace Engineering, Robotics, Automation, and Artificial Intelligence. * _Computer-Vision Tasks_ (i) The vast majority of research analyzed propose only solutions to tackle some kind of task and not a complete solution for a fully autonomous vision-based UAV. This fact, which has allowed us to structure the results in different categories, can be considered as negative since the aerial vehicles described fail to perform completely autonomous flights in real environments. However, it is foreseeable that the presence of completely autonomous UAVs based on computer vision will be common in the coming years, considering the rapid development and the annual trend of this research field. (ii) In this sense, the focus of research is shifting from relatively simpler operations, such as location and stabilization, to more complex solutions which are closer to the concept of autonomous UAV, such as following moving targets or trajectories defined by visual references, as well as solutions for the detection and avoidance of both static and dynamic obstacles, so as to carry out a collision-free flight. * _Type of UAV, Vision System and Validation Process_ (i) The most widely used UAVs belong to the rotary-wing class. With regard to this type of aerial vehicle, which is principally characterized by the fact that they permit vertical take-off and landing, suspended flight, and greater maneuverability than fixed-wing vehicles, the number of papers presenting solutions specifically designed for or tested on them has not stopped growing in this century. Quadrotors and (to a lesser extent) helicopters are the most frequent vehicles within this class of UAV. (ii) The most widely used vision system is a single camera installed on board the UAV. This configuration is present in almost 80% percent of the works analyzed. Secondly, stereo systems have also been used in a significant number of cases. Again, the annual trend shows the predominance of mono-camera vision systems against multi-camera configurations. (iii) More than 54% of the works analyzed have included flight tests in their validation process. However, a non-negligible number (around 25%) have validated their proposals only through the use of simulation tests. Nevertheless, in this case, not only have numerical simulations been employed, but also virtual reality environments, in addition to hardware- and image-in-the-loop simulations. Figure 19: Papers per flight conditions. After these general conclusions concerning the systematic mapping, we should state that we believe that the study carried out here is of great value since it has allowed the analysis of practically two decades of research. It should, therefore, be of great help to researchers who are starting to develop solutions for UAVs using computer vision, and for experts who wish to consult previously presented solutions. In addition, the classification structure introduced in this paper could be of great help for future reviews focused on the most recent years or certain types of operations, UAV classes, or validation processes carried out during real flights. These future studies may address technical issues in greater depth that are beyond the scope of a systematic mapping study. Lastly, it is necessary to center the discussion in the concept of assistant aerial robots, our personal research interest which led to performing this systematic mapping study. This way, and considering the information we have analyzed performing this study, a series of conclusions have been drawn to focus our future developments.