Research on Indoor Target Positioning System Based on Image Feature Extraction and Recognition

In view of the complexity of indoor environment and the limitations of existing positioning methods on indoor target positioning, this paper uses improved ORB algorithm to replace the target detection and recognition system on the market based on SURF or SIFT algorithm, relying on the target detection data in the real indoor scene. The system takes the image characteristics of the target as the main basis for recognition, and optimizes the indoor positioning accuracy through image recognition and positioning technology, hardware verification and evaluation. The system can effectively reduce the amount of image processing, optimize the target feature extraction, and improve the recognition accuracy. The research on this system in this paper is expected to be applied to a variety of practical scenes, such as improving the indoor positioning accuracy of UAVs, and providing useful reference for improving the positioning accuracy of a variety of large indoor scenes.