# Computer Vision in Autonomous Unmanned Aerial Vehicles--A Systematic Mapping Study
## 2 Methods
### Classification
#### 2.5.1 Vision-Based Task  
With regard to RQ2 (_Use of Computer Vision_), each paper was classified in one of the following main categories:  
* _Visual Navigation_: The navigation task includes determining the aircraft's position and orientation (e.g., [17; 18; 19]). Sensors are, therefore, required to measure the aircraft's state and to sense the flight environment. Here, the main feature when compared to those of other solutions is the use of a vision system as the main sensor, which may be complemented by other sensors (e.g., inertial measurement units, global positioning systems) in order to correct the visual estimation. Two examples are shown in Figure 4. These are a quadrotor using artificial visual markers to estimate its position in an indoor environment (see a.1), and a vision-based terrain referenced navigation approach, in which aerial images are compared with a digital terrain elevation database to estimate the UAV's position (see a.2).
* _Vision-Based Control_: The aircraft's position and orientation are controlled on the basis of the information captured by vision sensors and subsequently processed by computer vision algorithms (e.g., [20; 21; 22]). The vision-based control of UAVs began in the 1990s. Since then, several solutions have been proposed in order to address operations such as stabilization to maintain a constant altitude and/or a straight flight (see b.1), and maneuver control to guide a UAV so that it performs a specific and precise movement, such as positioning with respect to a visual mark (see b.2). When the information obtained by a vision system is integrated into the control loop directly, this is called _visual servoing_. In this case, the control law depends directly on the error signal obtained from visual information.
* _Vision-Based Tracking/Guidance_: In vision-based tracking or guidance, the UAV control system is designed to perform a flight based on relative navigation with respect to a target--usually in motion--or a flight path defined by a series of visual references or features (e.g., [23; 24; 25]). A vision-based system must, therefore, be able to detect the target or visual reference, estimate its position and determine the actions required to control the UAV's flight path. Two examples are presented in Figure 4. There is a mobile target tracking by a fixed-wing UAV (see c.1) and a quadrotor following a visual path defined by images of a previously known environment (see c.2).
* _Vision-Based Sense-and-Avoid (SAA)_: A completely autonomous navigation requires the ability to detect and avoid both static and dynamic obstacles. When these tasks are performed on the basis of the information captured and processed by a vision system, we refer to vision-based sense-and-avoid (e.g., [26; 27; 28; 29]). The main objective is, therefore, to use one or more cameras to detect possible collisions with objects or even with other aircraft, and to determine the control  
Figure 3: Classification scheme.  
actions required to achieve a collision-free flight. Two examples of vision-based SAA operations are illustrated in Figure 4. We have a quadrotor detecting the approach to an obstacle in its flight path (see d.1), and a mapping-based autonomous flight of a quadrotor in a coal mine (see d.2).