Precise Unmanned Aerial Vehicle Visual Positioning Based on Neural Network

Abstract

Abstract Unmanned aerial vehicle (UAV) positioning provides a means for human beings to achieve ubiquitous positioning and target tracking in low-altitude areas. The current UAVs mainly rely on satellite navigation and positioning, and airborne inertial devices (gyro) to complete positioning, which makes its signal easy to be blocked and leads to errors accumulation. However, visual positioning is characterized with high local positioning accuracy and good continuity. Therefore, visual positioning is integrated with UAV positioning in this study. First of all, UAV spatial reference acquisition and neural network are studied to analyze the shortcomings of the current UAV positioning technology in application and the necessity of integrating neural network. Second, a precise visual positioning algorithm based on neural network is presented. Based on the neural network model with image matching and position determination, the UAV visual positioning algorithm can be optimized and the redundancy of the target image can be reduced. What’s more, by establishing the experimental environment with Matlab, it is proved that the positioning accuracy can be improved with the algorithm proposed in this study.