## 4 Indoor AR Positioning Techniques  
In this section, we delve into the initialization of smartphone location and orientation information when launching indoor AR navigation. We describe the conditions for recognizing image markers as position reference markers and acquiring high-precision location and angle information. By converting this information into the global coordinates of the entire museum, we perform integrated processing of local coordinate spaces. Finally, we present a solution to correct the accumulative positioning error of image tracking with periodical calibration. Although high-precision spatial tracking (on the order of centimeters) is necessary for indoor navigation with camera-based systems, the standard approach initializes the coordinate space based on the camera's initial position and orientation at launch, with the \(z\)-axis aligned with the camera's orientation. This local coordinate system differs from the global reference frame used to register objects within indoor space. To leverage these existing objects in navigation tasks, it becomes crucial to convert the camera's position from the local frame to the global reference system. This section outlines the procedure for performing such a conversion. The present study leverages the ARKit framework [35] by Apple Inc. for spatial tracking. Specifically, we employ the ARWorldTrackingConfiguration class [34], which offers robust real-world device pose estimation by fusing data from the camera sensor with information from Core Motion [37], effectively normalizing variations across different cameras and devices. Such normalization mitigates the impact of device heterogeneity. It accommodates wide-angle camera modes but currently does not support ultra-wide-angles by considering cross-device compatibility. The ARWorldTrackingConfiguration class facilitates global spatial localization by recognizing image markers and tracking planar surfaces. Although ARKit provides a robust framework for spatial tracking, its effectiveness is bounded by the scope of the area it can accurately cover. Recognizing this limitation, our study aims to explore  
Figure 2: Example of indoor AR navigation prototype user interfaces as an iOS application. and address the challenges associated with expanding the coverage of spatial tracking to encompass larger areas. By doing so, we aspire to extend the potential applications of AR, making it more versatile and useful for a wider range of scenarios. Acquisition of Initial Position and Orientation through Image-Tracking of a Location Reference Marker  
The first step in indoor AR positioning involves acquiring the initial position at system start-up by using the smartphone camera, as detailed in Section A of Figure 1. The process measures the angle and distance to the marker board, such as a signboard that has been preset as a location reference. A floor map board is used for this purpose in the experimental setup. These measurements are achieved by recognizing and tracking the marker board (as indicated in Figure 1, A2). This methodology preceding the era of smartphones was similarly employed by [PERSON] et al. [38]. Our experimental application leverages the image-tracking functions provided by the ARWorldTrackingConfiguration class within the ARKit library [34; 35]. A preliminary experiment was conducted to confirm the performance of position and angle measurement using the smartphone's camera. A polar coordinate system was established, with its origin at the marker board's center point and its zero-degree axis aligned with the image's normal vector. Signs for the observation points were systematically placed on the floor at 15-degree increments to the left and right (up to 60 degrees) and at radial distances, increasing by 15 cm (up to 120 cm). Excluding the 0 cm point, 40 observation points were marked with approximately 2 cm lengths of tape. The smartphone camera was positioned at each observation point, with its height aligned to the reference image's center and its orientation facing that center point.