An AR Map Virtual-Real Fusion Method Based on Element Recognition

The application of AR to explore augmented map representation has become a research hotspot due to the growing application of AR in maps and geographic information in addition to the rising demand for automated map interpretation. Taking the AR map as the research object, this paper focuses on AR map tracking and registration and the virtual–real fusion method based on element recognition. It strives to establish a new geographic information visualization interface and application model. AR technology is applied to the augmented representation of 2D planar maps. A step-by-step identification and extraction method of unmarked map elements are designed and proposed based on the analysis of the characteristics of planar map elements. This method combines the spatial and attribute characteristics of point-like elements and line-like elements, extracts the color, geometric features, and spatial distribution of map elements through computer vision methods, and completes the identification and automatic extraction of map elements. The multi-target image recognition and extraction method based on template and contour matching, and the line element recognition and extraction method based on color space and area growth are introduced in detail. Then, 3D tracking and registration is used to realize the unmarked tracking and registration of planar map element images, and the AR map virtual–real fusion algorithm is proposed. The experimental results and results of an analysis of stepwise identification and extraction of unmarked map elements and map virtual–real fusion reveal that the stepwise identification of unmarked map elements and map model virtual–real fusion studied in this paper is effective. Through the analysis of map element step-by-step recognition efficiency and recognition rate, it is proved that the element step-by-step method in this paper is fast, its recognition efficiency meets the AR real-time requirements, and its recognition accuracy is high.