In this paper, we propose a method to calculate the position of interest objects in the image by matching the visual image with the three-dimensional model. Through the sensor and 3D model from the height accuracy of geographic information, to eliminate the error and improve the positioning accuracy. In this method, two visible light sensors are used for positioning, SIFT algorithm is used for image matching, and feature points are obtained. The obtained feature points are mapped to the known 3D model. Using the mapped geographic information, 3D model coordinate information and geographic information, the corresponding relationship between 3D model coordinates and original image coordinates is obtained. Finally, the precise positioning information is obtained through the pixel coordinates of the target in the image. Compared with the existing algorithms, the proposed method can achieve high-precision real-time geographic positioning, and the positioning calculation process is simple, which can effectively improve the positioning efficiency.
(Visual Simultaneous Location and Mapping) VSLAM is the key technology of computer vision and equipment intelligence. Firstly, this paper introduces the main algorithms and historical evolution of VSLAM, and summarizes the existing algorithms from the front-end and back-end classification, advantages and disadvantages. Subsequently, new research directions emerged in recent years are introduced, focusing on the combination of depth, semantics and multi-robots. Finally, the existing problems are summarized, and the future direction of development is prospected.
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