With the extensive application of big spatial data and the emergence of spatial computing, augmented reality (AR) map rendering has attracted significant attention. A common issue in existing solutions is that AR-GIS systems rely on different platform-specific graphics libraries on different operating systems, and rendering implementations can vary across various platforms. This causes performance degradation and rendering styles that are not consistent across environments. However, high-performance rendering consistency across devices is critical in AR-GIS, especially for edge collaborative computing. In this paper, we present a high-performance, platform-independent AR-GIS rendering engine; the augmented reality universal graphics library (AUGL) engine. A unified cross-platform interface is proposed to preserve AR-GIS rendering style consistency across platforms. High-performance AR-GIS map symbol drawing models are defined and implemented based on a unified algorithm interface. We also develop a pre-caching strategy, optimized spatial-index querying, and a GPU-accelerated vector drawing algorithm that minimizes IO latency throughout the rendering process. Comparisons to existing AR-GIS visualization engines indicate that the performance of the AUGL engine is two times higher than that of the AR-GIS rendering engine on the Android, iOS, and Vuforia platforms. The drawing efficiency for vector polygons is improved significantly. The rendering performance is more than three times better than the average performances of existing Android and iOS systems.

Geographic registration (geo-registration) is a crucial foundation for augmented reality (AR) map applications. However, existing methods encounter difficulties in aligning spatial data with the ground surface in complex outdoor scenarios. These challenges make it difficult to accurately estimate the geographic north orientation. Consequently, the accuracy and robustness of these methods are limited. To overcome these challenges, this paper proposes a rotation-invariant estimation method for high-precision geo-registration in AR maps. The method introduces several innovations. Firstly, it improves the accuracy of generating heading data from low-cost hardware by utilizing Real-Time Kinematic GPS and visual-inertial fusion. This improvement contributes to the increased stability and precise alignment of virtual objects in complex environments. Secondly, a fusion method combines the true-north direction vector and the gravity vector to eliminate alignment errors between geospatial data and the ground surface. Lastly, the proposed method dynamically combines the initial attitude relative to the geographic north direction with the motion-estimated attitude using visual-inertial fusion. This approach significantly reduces the requirements on sensor hardware quality and calibration accuracy, making it applicable to various AR precision systems such as smartphones and augmented reality glasses. The experimental results show that this method achieves AR geo-registration accuracy at the 0.1-degree level, which is about twice as high as traditional AR geo-registration methods. Additionally, it exhibits better robustness for AR applications in complex scenarios.

Increasingly complex vector map applications and growing multi-source spatial data pose a serious challenge to the accuracy and efficiency of vector map visualization. It is true especially for real-time and dynamic scene visualization in mobile augmented reality, with the dramatic development of spatial data sensing and the emergence of AR-GIS. Such issues can be decomposed into three issues: accurate pose representation, fast and precise topological relationships computation and high-performance acceleration methods. To solve these issues, a novel quaternion-based real-time vector map visualization approach is proposed in this paper. It focuses on precise position and orientation representation, accurate and efficient spatial relationships calculation and acceleration parallel rendering in mobile AR. First, a quaternion-based pose processing method for multi-source spatial data is developed. Then, the complex processing of spatial relationships is mapped into simple and efficient quaternion-based operations. With these mapping methods, spatial relationship operations with large computational volumes can be converted into efficient quaternion calculations, and then the results are returned to respond to the interaction. Finally, an asynchronous rendering acceleration mechanism is also presented in this paper. Experiments demonstrated that the method proposed in this paper can significantly improve vector visualization of the AR map. The new approach, when compared to conventional visualization methods, provides more stable and accurate rendering results, especially when the AR map has strenuous movements and high frequency variations. The smoothness of the user interaction experience is also significantly improved.

High-performance spatial target snapping is an essential function in 3D scene modeling and mapping that is widely used in mobile augmented reality (MAR). Spatial data snapping in a MAR system must be quick and accurate, while real-time human–computer interaction and drawing smoothness must also be ensured. In this paper, we analyze the advantages and disadvantages of several spatial data snapping algorithms, such as the 2D computational geometry method and the absolute distance calculation method. To address the issues that existing algorithms do not adequately support 3D data snapping and real-time snapping of high data volumes, we present a new adaptive dynamic snapping algorithm based on the spatial and graphical characteristics of augmented reality (AR) data snapping. Finally, the algorithm is experimented with by an AR modeling system, including the evaluation of snapping efficiency and snapping accuracy. Through the experimental comparison, we found that the algorithm proposed in this paper is substantially improved in terms of shortening the snapping time, enhancing the snapping stability, and improving the snapping accuracy of vector points, lines, faces, bodies, etc. The snapping efficiency of the algorithm proposed in this paper is 1.6 times higher than that of the traditional algorithm on average, while the data acquisition accuracy based on the algorithm in this paper is more than 6 times higher than that of the traditional algorithm on average under the same conditions, and its data accuracy is improved from the decimeter level to the centimeter level.

With the diversification of terminal equipment and operating systems, higher requirements are placed on the rendering performance of maps. The traditional map rendering engine relies on the corresponding operating system graphics library, and there are problems such as the inability to cross the operating system, low rendering performance, and inconsistent rendering style. With the development of hardware, graphics processing unit (GPU) appears in various platforms. How to use GPU hardware to improve map rendering performance has become a critical challenge. In order to address the above problems, this study proposes a cross-platform and high-performance map rendering (Graphics Library engine, GL engine), which uses mask drawing technology and texture dictionary text rendering technology. It can be used on different hardware platforms and different operating systems based on the OpenGL graphics library. The high-performance map rendering engine maintains a consistent map rendering style on different platforms. The results of the benchmark experiments show that the performance of GL engine is 1.75 times and 1.54 times better than the general map rendering engine in the iOS system and in the Android system, respectively, and the rendering performance for vector tiles is 11.89 times and 9.52 times better than rendering in the Mapbox in the iOS system and in the Android system, respectively.