A Reliable Indoor Navigation System for Humans Using AR-based Technique
This addresses the problem of confusing indoor navigation for users in campuses and small areas, though it is incremental as it integrates existing AR and pathfinding methods.
The paper tackled the problem of unreliable indoor navigation by applying an AR-based technique with Vuforia Area Target and the A* algorithm, achieving a solution two to three times faster than Dijkstra's algorithm in smaller search spaces and showing improved accuracy, user experience, and efficiency.
Reliable navigation systems are not available indoors, such as in campuses and small areas. Users must depend on confusing, time-consuming static signage or floor maps. In this paper, an AR-based technique has been applied to campus and small-site navigation, where Vuforia Area Target is used for environment modeling. AI navigation's NavMesh component is used for navigation purposes, and the A* algorithm is used within this component for shortest path calculation. Compared to Dijkstra's algorithm, it can reach a solution about two to three times faster for smaller search spaces. In many cases, Dijkstra's algorithm has difficulty performing well in high-complexity environments where memory usage grows and processing times increase. Compared to older approaches such as GPS, real-time processing and AR overlays can be combined to provide intuitive directions for users while dynamically updating the path in response to environmental changes. Experimental results indicate significantly improved navigation accuracy, better user experience, and greater efficiency compared to traditional methods. These results show that AR technology integrated with existing pathfinding algorithms is feasible and scalable, making it a user-friendly solution for indoor navigation. Although highly effective in limited and defined indoor spaces, further optimization of NavMesh is required for large or highly dynamic environments.