# Computer Vision in Autonomous Unmanned Aerial Vehicles--A Systematic Mapping Study
## 3 Results
### RQ2: How Is Computer Vision Used in Autonomous UAVs?
#### 3.2.2 Vision-Based Task  
Category 1. Visual Navigation  
Firstly, it should be kept in mind that the flight control of a UAV depends principally on knowing where the UAV is and where it wants to go. Knowledge of the aircraft's state is, therefore, essential. Therefore, it seems logical that navigation solutions have been the focus of much research carried out to date. The subcategories considered within the first category are: _1.1. Self-Localization_ for those papers that use computer vision to determine the aircraft's position within the flight environment (19 papers); _1.2. State Estimation_ for papers focused on solutions by which to compose the aircraft's state using visual information (based principally on visual odometry); and, _1.3. Based on SLAM_ to classify vision-based solutions that simultaneously determine the aircraft's localization and build a map or model of the flight environment (12 papers). At this point, it is important to note that other sensors are frequently used to complement and correct the visual ones. The most common are global positioning systems (only for outdoors), inertial measurement units, and altimeters. Unfortunately, this information is not clearly detailed in some papers analyzed, especially papers in which validation trials do not consist of real flights. For this reason, the study of complementary sensors had not been considered in this mapping study. However, it would be interesting to address this issue in future more in-depth reviews. Category 2. Vision-Based Control  
This is the category in which the greatest number of papers has been found. In addition, its tendency toward annual growth is highly notable. As mentioned above, vision-based control consists of determining the actions required to control the aircraft's position and orientation by using visual information as a reference. In this second category, the papers have been classified, according to their control purpose, into: _2.1. Stabilization_ when visual information is integrated into the control system of the aircraft to stabilize its flight; principally using the skyline as a reference, or using algorithms for the detection and compensation of movement; _2.2. Control of Maneuvers_ when the objective is to regulate the UAV's position in order to perform accurate movements such as positioning in relation to a visual landmark; and _2.3. Autonomous Landing_ when the information provided by vision sensors is integrated into the landing control system (papers focused on the detection of the landing site and the consequent estimation of the aircraft's pose to assist in landing have also been considered here). The results show that stabilization is, without any doubt, the least frequent topic with only four papers, while both the control of maneuvers and landing (a specific type of maneuver) are the subcategories with a greater number of papers (24 and 27, respectively). At this moment, it is important to introduce visual servoing [88], which means that the motion of a UAV (or a robot, in general) is controlled with respect to a visual target, defined by features or artificial landmarks. Visual information is directly integrated into the feedback control in this widely used control approach. Three main methods are considered [163]: (a) position-based visual servoing (PBVS), or 3D visual servoing, which involves the reconstruction of the target pose with respect to the camera, (b) image-based visual servoing (IBVS), or 2D visual servoing, which aims to control the dynamics of features directly in the image plane, and (c) hybrid visual servoing, or 2 1/2D visual servoing, which is based on combining visual features obtained directly from the image, and features expressed in the Euclidean space. Of these three methods, the most commonly used is IBVS, in which the control action is directly determined from the error signal obtained by comparing the actual image captured by the camera and a reference image. This method has been used principally in maneuvers for rotatory-wing UAVs and in landing for fixed-wing UAVs.