XAMKNAV: An AR-based multi-floor indoor navigation prototype
Gauci, Mac Patrick (2025)
2025
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2025060520674
https://urn.fi/URN:NBN:fi:amk-2025060520674
Tiivistelmä
Indoor navigation remains a persistent challenge in multi-floor public buildings where traditional GPS systems fail to provide accurate positioning. This thesis explores the development of XAMKNAV, a mobile Augmented Reality (AR) application aimed at enhancing indoor wayfinding within the South-Eastern Finland University of Applied Sciences (Xamk) Kotka campus. The prototype leverages Unity and AR Foundation to deliver real-time navigational guidance through QR-based localisation and accessibility-aware pathfinding.
The objective of the study was to create an infrastructure-free solution capable of guiding users intuitively between floors, with support for both elevator and stair-based routing. QR codes serve as static anchors for position reinitialisation, and an A* algorithm provides efficient, accessible paths. A synchronised minimap was also developed to improve spatial awareness during navigation.
The research employed a mono-method qualitative approach using observations, informal discussions, and post-test questionnaires to assess the system’s usability. Participants reported improved confidence in navigating unfamiliar environments, particularly praising the AR navigation line and integrated minimap. Some technical limitations, such as tracking drift and lack of automated floor transition prompts, were identified.
Overall, the study demonstrates that AR can provide lightweight, scalable indoor navigation without reliance on external infrastructure. The findings offer a strong foundation for further development toward broader deployment across educational and public institutions.
The objective of the study was to create an infrastructure-free solution capable of guiding users intuitively between floors, with support for both elevator and stair-based routing. QR codes serve as static anchors for position reinitialisation, and an A* algorithm provides efficient, accessible paths. A synchronised minimap was also developed to improve spatial awareness during navigation.
The research employed a mono-method qualitative approach using observations, informal discussions, and post-test questionnaires to assess the system’s usability. Participants reported improved confidence in navigating unfamiliar environments, particularly praising the AR navigation line and integrated minimap. Some technical limitations, such as tracking drift and lack of automated floor transition prompts, were identified.
Overall, the study demonstrates that AR can provide lightweight, scalable indoor navigation without reliance on external infrastructure. The findings offer a strong foundation for further development toward broader deployment across educational and public institutions.