ARLO: Augmented Reality Localization Optimization for Real-Time Pose Estimation and Human–Computer Interaction

Accurate and real-time outdoor localization and pose estimation are critical for various applications, including navigation, robotics, and augmented reality. Apple’s ARKit, a leading AR platform, employs visual–inertial odometry (VIO) and simultaneous localization and mapping (SLAM) algorithms to enable localization and pose estimation. However, ARKit-based systems face positional bias when the device’s camera is obscured, a frequent issue in dynamic or crowded environments. This paper presents a novel approach to mitigate this limitation by integrating position bias correction, context-aware localization, and human–computer interaction techniques into a cohesive interactive module group. The proposed system includes a navigation module, a positioning module, and a front-end rendering module that collaboratively optimize ARKit’s localization accuracy. Comprehensive evaluation across a variety of outdoor environments demonstrates the approach’s effectiveness in improving localization precision. This work contributes to enhancing ARKit-based systems, particularly in scenarios with limited visual input, thereby improving user experience and expanding the potential for outdoor localization applications. Experimental evaluations show that our method improves localization accuracy by up to 92.9% and reduces average positional error by more than 85% compared with baseline ARKit in occluded or crowded outdoor environments.