Geo-registration is a fundamental process seamlessly integrating digital information within the physical world in Mobile Augmented Reality (MAR). Achieving high precision, real-time capability, and strong adaptability in geo-registration is crucial for the effective functioning of MAR applications, especially in outdoor environments. However, existing methods frequently struggle with inaccuracies in long-distance positioning and latency of pose estimation, compounded by their sensitivity to scale changes of outdoor environment. This study addresses these challenges by proposing a novel continuous and real-time MAR geo-registration method for outdoor applications. Our approach integrates real-time kinematic Global Navigation Satellite System (RTK-GNSS) fusion with geodesic equations and rotation invariance estimation. This method substantially surpasses traditional methods, achieving 0.05 m virtual-real position accuracy (approximately six times better) and under 0.2° pose accuracy (nearly a fivefold improvement). Additionally, it exhibits superior robustness in complex MAR scenarios. Beyond improved accuracy, this method reduces the reliance on high-quality sensor hardware and precise calibration, making it suitable for various AR systems, including smartphones and tablets.

中文翻译：

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增强移动 AR 中的室外远距离匹配：一种连续和实时的地理配准方法


地理配准是在移动增强现实 （MAR） 中将数字信息无缝集成到物理世界中的基本过程。在地理配准中实现高精度、实时性和强大的适应性对于 MAR 应用程序的有效运行至关重要，尤其是在户外环境中。然而，现有方法经常面临远距离定位不准确和姿态估计延迟的问题，再加上它们对室外环境尺度变化的敏感性。本研究通过提出一种用于户外应用的新型连续实时 MAR 地理配准方法来应对这些挑战。我们的方法将实时运动学全球导航卫星系统 （RTK-GNSS） 融合与测地线方程和旋转不变性估计相结合。这种方法大大超越了传统方法，实现了 0.05 m 的虚实位置精度（大约提高了 6 倍）和低于 0.2° 的姿势精度（几乎提高了 5 倍）。此外，它在复杂的 MAR 场景中表现出卓越的鲁棒性。除了提高精度外，这种方法还减少了对高质量传感器硬件和精确校准的依赖，使其适用于各种 AR 系统，包括智能手机和平板电脑。