## 6 Implementation Experiments
### Positioning Accuracy Evaluation in Spatial Tracking with VIO  
In indoor AR navigation, the accuracy of positioning is expected to decrease with prolonged use of spatial tracking with VIO. This decrease in accuracy is significant for the design of such services. This subsection presents the results of an experiment designed to evaluate spatial accuracy and its rate of decline using the original indoor AR navigation  
Figure 9: Floor map of the Mineral Industry Museum (1F), Akita University.  
application developed by us. The experiment involved measuring errors in spatial tracking with a camera within the real-world environment of a museum hallway. For this experiment, signs made of tape--approximately 2 cm in width--were placed on the floor at 1 m intervals to outline the intended route. The actual path was navigated using an AR app, and the discrepancy between the tape's real-world location and its position as identified in the digital space by the AR app was measured with a ruler.  
Figure 10 shows the results of the spatial accuracy evaluation of positioning using spatial tracking with VIO, with Apple Inc.'s iPhone 11 as the target device (with the functions of the ARWorldTrackingConfiguration class in Apple Inc.'s ARKit library). In this experiment, the user walked at a normal speed of approximately 1.0 m/sec while holding the smartphone almost vertically in their hand and pointing the camera in the direction that they were moving. As the distance they moved increased, the positioning error increased, resulting in an error of approximately 1.6 m when 160 m was covered. The convolutional error was about 1%, which indicated a linear increase in the positioning error. With this level of error, indoor AR positioning of sufficient quality is feasible. However, because the positioning error accumulates as the distance moved increases, periodic positioning calibration is required.  
Figure 11a visualizes an example of the raw data obtained using spatial tracking with VIO (sample rate: 0.1 s). Smoothing is necessary because outliers sometimes occur. Figure 11b shows an example of smoothing using the median value for the previous 1.0 s. Smoothing overcomes the problem of positioning error outliers and provides more appropriate and stable location information, thus avoiding straggling when providing LBS services. The drawback of smoothing is the delay in error detection; however, this is not a major problem in use cases involving walking.  
Figure 11: Characteristics of the raw data (0.1-s sampling) of VIO-based positioning and an improvement using a median filter: (**a**) Raw data (sampling rate: 0.1 s); (**b**) Data smoothed using the median from a set of data generated in the previous 1.0 s.  
Figure 10: Example of the positioning accuracy evaluation of VIO-based spatial tracking.  
Figure 12 shows how much the error in spatial tracking by VIO varies depending on how the user moves with the smartphone. As can be seen, error accumulation varies depending on the user's walking speed, the swaying of the smartphone, and even the model of the smartphone. Although the errors vary depending on how the user uses the smartphone, it is possible to estimate the movement error to some extent if we know the user's preferences (walking style, smartphone model, etc.).