Smartphone-based indoor navigation services are desperately needed in indoor environments. However, the adoption of them has been relatively slow, due to the lack of fine-grained and up-to-date indoor maps, or the potentially high deployment and maintenance cost of infrastructure-based indoor localization solutions. This work proposes ViNav, a scalable and cost-efficient system that implements indoor mapping, localization, and navigation based on visual and inertial sensor data collected from smartphones. ViNav applies structure-from-motion (SfM) techniques to reconstruct 3D models of indoor environments from crowdsourced images, locates points of interest (POI) in 3D models, and compiles navigation meshes for path finding. ViNav implements image-based localization that identifies users' positions and facing directions, and leverages this feature to calibrate dead-reckoning-based user trajectories and sensor fingerprints collected along the trajectories. The calibrated information is utilized for building more informative and accurate indoor maps, and lowering the response delay of localization requests. According to our experimental results in a university building and a supermarket, the system works properly and our indoor localization achieves competitive performance compared with traditional approaches: in a supermarket, ViNav locates users within 2 seconds, with a distance error less than 1 meter and a facing direction error less than 6 degrees.

Preface would have been impossible without the help and support of the people closest to me. I send my warmest thanks to my loving family for their endless support, patience, and encouragement. My lovely fiancée Eglė stood by me and supported me during the toughest times and inspired to continue with my work. Therefore, I dedicate this thesis to them.

Augmented reality (AR) applications have recently emerged for entertainment and educational purposes and have been proposed to have positive effects on social interaction. In this study, we investigated the impact of a mobile, indoor AR feature on sociability, entertainment, and learning. We conducted a field experiment using a quiz game in a Finnish science center exhibition. We divided participants (N = 372) into an experimental group (AR app users) and two control groups (non-AR app users; pen-and-paper participants), including 28 AR users of follow-up interviews. We used Kruskal–Wallis rank test to compare the experimental groups and the content analysis method to explore AR users’ experiences. Although interviewed AR participants recognized the entertainment value and learning opportunities for AR, we did not detect an increase in perceived sociability, social behavior, positive affect, or learning performance when comparing the experimental groups. Instead, AR interviewees experienced a strong conflict between the two different realities. Despite the engaging novelty value of new technology, performance and other improvements do not automatically emerge. We also discuss potential conditional factors. Future research and development of AR and related technologies should note the possible negative effects of dividing attention to both realities.

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