Research on Optimization of Visual Navigation Algorithm for Real-Time Obstacle Avoidance of UAV Moving Target Tracking

In recent years, UAV target tracking and real-time obstacle avoidance are urgent problems in UAV industry. In the UAV visual navigation system, how to realize real-time obstacle avoidance in the process of moving target tracking is an important part of successful tracking. In this study, the image of the tracked object is collected by the monocular camera, and the motion target is predicted and corrected based on the Kalman filter algorithm to determine the size and direction of the search and tracking object. The binocular camera collects the real-time image information of the UAV in the forward direction. The object’s distance can be determined by reliable disparity values and spatial depth information obtained through the camera. The moving speed of the obstacle relative to the camera at each moment can be obtained through the SIFT-based optical flow method. Experimental results show that the method can effectively guide the UAV to continuously and accurately track the target object, and avoid obstacles in the forward direction in the tracking process.