# Autonomous Target Tracking of UAV Using High-Speed Visual Feedback
## 1 Introduction  
Unmanned aerial vehicles (UAVs), which are often called drones or multicopters, have become a popular research topic because of their high autonomy and flexibility. To achieve autonomous flight of a UAV, it is important to obtain its position and attitude in real time. In most current UAVs, an inertial measurement unit (IMU) is used to acquire the attitude, and the Global Positioning System (GPS) is used to acquire the position. These signals are then fused by a Kalman filter. However, GPS is not suitable in indoor environments. Even outdoors, it is also unsuitable if there are many obstacles. When the GPS signal is blocked, the UAV easily loses its locational information. On the other hand, flying creatures such as birds and insects use mainly visual information for their flight control [1]. In particular, in environments with many obstacles, visual information plays an important role in collision avoidance [2; 3]. In order to realize completely autonomous flight of a UAV in any environment, a robust and stable visual feedback flight control method is indispensable. In conventional research on vision systems for UAVs, visual simultaneous localization and mapping (Visual SLAM) and visual odometry (VO) for vision-based navigation are the main topics. The V-SLAM technique constructs a live map of the surrounding environment passed by a UAV, and the UAV localizes itself relative to this constructed map [4; 5]. VO estimates the egomotion of a UAV using successive captured images instead of constructing a complete environmental map of the UAV [6]. These studies focused on the recognition of a 3D environment, and UAVs were controlled based on the recognition result. However, the computational cost of V-SLAM is high, and the processing speed is not very high. On the other hand, in flying organisms, real-time visual information is used to avoid collisions and fly in narrow spaces. In such cases, it is not necessary to obtain a detailed 3D environment map in world coordinates, and a UAV can fly by visual servoing even if only the relative position and orientation between the UAV's body and the obstacle is obtained. The process of accurate 3D environment recognition is omitted, enabling responsive and quick flight. We think that the performance of such responsive visual servoing in conventional UAV research is still not high enough. There have been studies using simple optical flow sensors [7; 8], ultrasonic sensors [9; 10], and laser imaging detection and ranging (LIDAR) sensors for self-navigation [11; 12]. However, there are not many examples using visual servoing control for UAVs. This is due to the low frame rate of conventional vision systems and the heavy computational cost of visual processing. Our group has developed various types of high-speed vision systems with processing speeds of around 500 Hz to 1000 Hz [13]. In this paper, we developed a new lightweight high-speed vision system for UAVs and visual servo control for quick target tracking. The proposed vision system could recognize the surrounding environment in a short time and could immediately transmit control signals to the flight controller. Figure 1 shows the difference between the relative-position-based visual servoing used in this paper and the conventional approach. The relative-position-based visual servoing estimates the relative position and orientation to the target for visual servoing instead of collecting complete environmental information for making a control strategy. This paper is organized as follows: In Section 2, research on UAV control using visual information is described. In Section 3, we introduce the system configuration of our UAV and the moving target used in the experiment. In Section 4, we explain the process of image processing for estimating the relative pose between a target and the UAV. In Section 5, we introduce the principle of the relative-position-based visual servoing and the controller design for a position hold task.