###### Abstract  
This study presents a novel framework for improving indoor augmented reality (AR) navigation with modern smartphone technology, which is achieved by addressing two major challenges: managing large absolute coordinate spaces and reducing error accumulation in camera-based spatial tracking. Our contribution is significant in two ways. First, we integrate geofencing with indoor navigation by considering spatial tracking errors, timing for audio guidance, and dynamic 3D arrow visualization for effective local-to-global spatial coordinate transformation. This method achieves precise local positioning and seamlessly integrates with larger spatial contexts, overcoming the limitations of current AR systems. Second, we introduce a periodic image-based calibration approach to minimize the inherent error accumulation in camera-based tracking, enhancing accuracy over longer distances. Unlike prior studies focusing on individual technologies, our work explores the software architecture of indoor AR navigation by providing a comprehensive framework for its design and practical use. The practicality of our approach is validated through the implementation of a smartphone application at the Mineral Industry Museum of Akita University, highlighting the limitations of component technologies and demonstrating our framework's effectiveness.  
indoor navigation; augmented reality; spatial tracking; geofencing; accumulated positioning error calibration +
Footnote †: journal: Medical  
accuracy of GNSS can vary significantly, typically between 2 and 6 m under optimal conditions [7]. It is considerably less effective in indoor and underground environments due to electromagnetic interference and signal reception challenges. Although Bluetooth and wireless personal area network (WPAN) beacons have been developed to enhance indoor IT services [8; 9; 10; 11; 12], the comprehensive and seamless integration of these technologies across extensive and interconnected indoor spaces remains an unfulfilled goal.  
The paper proposes a novel framework designed to surmount two primary obstacles to advancing indoor AR navigation technology: the effective navigation of extensive absolute coordinate spaces and the minimization of cumulative positioning errors in camera-based tracking systems. The framework utilizes visual-inertial odometry (VIO) [13; 14; 15], complemented by location reference image markers and a transformation from local-to-global spatial coordinates, to boost accuracy and reliability across broad coordinate spaces. By integrating a global spatial coordinate system, we enable the use of geofencing, a technology well-established in outdoor LBS [16], for indoor environments as well. We demonstrate the effectiveness and practicality of our framework through application development and testing in the Akita University Mineral Industry Museum (Tegata-Osawa, Akita, Japan) [17]. This research differentiates itself by offering a synergistic combination of VIO with image-based calibration and integrating a global spatial coordinate system, laying the groundwork for future advancements in developing practical indoor AR navigation systems.  
The present paper is structured as follows: Section 2 reviews the related work to position our research. Section 3 describes the indoor AR navigation framework, focusing on calibration with camera-based reference image recognition and spatial tracking. Section 4 explores the practice of indoor AR positioning in a real-world context, including the recognition of location reference images and the management of spatial tracking errors. Section 5 details the implementation of the navigation service, focusing on geofencing designs. Section 6 demonstrates a field test conducted at the Akita University Mining Museum. Section 7 discusses the results, challenges, and limitations. Finally, Section 8 concludes the paper by summarizing the study's contributions and proposing future research directions.