# Computer Vision in Autonomous Unmanned Aerial Vehicles--A Systematic Mapping Study
## 3 Results
### RQ2: How Is Computer Vision Used in Autonomous UAVs?
#### 3.2.1 Distribution and Annual Trend per Category  
The way in which the 144 papers are distributed in the four main categories is shown in Figure 9. The graph shows the number of papers in each main category and also details the classification that has been carried out considering the specific operation addressed. The results show that practically two of every three papers belong to the first two categories, 41 are focused on the design of visual solutions for UAV navigation (cat. 1) and 55 present control algorithms based on the information provided by a vision system (cat. 2). The remaining papers are divided into the two other categories: 33 and 15 papers focus on developing solutions for the visual guidance of a UAV (cat. 3) and solutions to allow a UAV to detect and avoid obstacles during its flight (cat. 4), respectively.  
The number of works published annually within each main category can be consulted in Figure 10. In general, an upward trend that is remarkable for the second and third categories can be appreciated. When considering the publication year, we can highlight that the earlier works appertain to the two first categories, while papers presenting vision-based solutions for the guidance or detection and avoidance of obstacles are more recent. In our opinion, this is a clear consequence of the fact that navigation and control are basic (essential) tasks to be dealt with before confronting more complex tasks, such as objective/obstacle tracking and avoidance. Flying autonomously and safely requires the design of more complex and robust control systems. Another noteworthy fact is that, except in the  
Figure 9: Category distribution over the database.  
first years (2000-2001), works on computer vision for UAVs have appeared annually, and, since 2013, the works have been distributed in the four categories. These data highlight the scientific community's current interest in the development of computer vision systems for different navigation and flight control operations.  
The data for the classification according to the specific operation can be consulted in Table 4. It shows the complete list of the 144 papers grouped into twelve subcategories (three for each main category). The operations considered and the results obtained are discussed below.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Category** & **N** & **\%** \\ \hline
**1. Visual Navigation** & \\
1.1. Self-Localization & **10** & 6.94\% \\
1.2. State Estimation & **19** & 13.19\% \\
1.3. Based on SLAM & **12** & 8.33\% \\ \hline
**2. Vision-Based Control** & \\
2.1. Stabilization & **4** & 2.78\% \\
2.2. Control of Maneuvers & **24** & 16.67\% \\
2.3. Autonomous Landing & **27** & 18.75\% \\
**3. Vision-Based** & \\
**Tracking/Guidance** & \\
3.1. Target Tracking & **17** & 11.81\% \\
3.2. Features/Path Tracking & **6** & 4.17\% \\
3.3. Autonomous Aerial & **10** & 6.94\% \\ Refueling (AAR) & \\ \hline
**4. Vision-Based** & \\
**Sense-and-Avoid (SAA)** & \\
4.1. Detection and & \\ Avoidance of Intruders & **5** & 3.47\% \\
4.2. Mapping-based & \\ Obstacle Avoidance & **6** & 4.17\% \\
4.3. Autonomous Flight and & \\ Obstacle Avoidance & **4** & 2.78\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: List of papers per category.  
Figure 10: Annual trend per category.