A Unified AR Navigation Framework for Indoor and Outdoor Environments Using Point Clouds and GPS

Indoor navigation remains a critical challenge for existing GPS based systems, particularly within large campuses, warehouses, retail complexes, and industrial facilities. This study introduces a fully integrated navigation framework that seamlessly unifies outdoor GPS based routing with indoor digital-twin navigation using point cloud data. The proposed system, developed in Unity, supports real time localization, augmented reality (AR) based guidance, and hotspot driven pathfinding in complex indoor environments.
Outdoor navigation is powered by GPS and Mapbox APIs for route computation, while indoor navigation employs Matterport generated digital twins combined with point cloud based spatial mapping to achieve high precision localization and mapping. The system autonomously detects transition boundaries between outdoor and indoor zones, dynamically switching navigation modes to maintain continuity.

Indoor navigation, Outdoor navigation, Digital twin, Point cloud, Unity 3D

1. Ahmed, S., Rahman, M., & Kim, D. (2023). Smart campus navigation using hybrid positioning technologies. IEEE Access, 11, 22145–22158. [Google Scholar] [Crossref]

2. Alarifi, A., Al-Salman, A., Alsaleh, M., et al. (2021). Ultra-wideband indoor positioning technologies: Analysis and recent advances. IEEE Communications Surveys & Tutorials, 23(2), 1049–1075. [Google Scholar] [Crossref]

3. Azuma, R. (2023). Location-based augmented reality navigation systems. IEEE Computer Graphics and Applications, 43(1), 45–54. [Google Scholar] [Crossref]

4. Batty, M. (2021). Digital twins. Environment and Planning B: Urban Analytics and City Science, 48(2), 1–6. [Google Scholar] [Crossref]

5. Billinghurst, M., Clark, A., & Lee, G. (2022). A survey of augmented reality technologies and applications. Foundations and Trends in Human–Computer Interaction, 15(2), 73–272. [Google Scholar] [Crossref]

6. Brena, R., Garcia-Vazquez, J., Galvan-Tejada, C., et al. (2021). Evolution of indoor positioning technologies. ACM Computing Surveys, 54(1), 1–33. [Google Scholar] [Crossref]

7. Brown, T., Wilson, A., & Patel, S. (2021). Mobile augmented reality navigation systems: Design and evaluation. IEEE Virtual Reality Conference, 312–320. [Google Scholar] [Crossref]

8. Cadena, C., Carlone, L., Carrillo, H., et al. (2020). Simultaneous localization and mapping: Current trends and future directions. IEEE Transactions on Robotics, 36(6), 1–19. [Google Scholar] [Crossref]

9. Chen, L., Zhang, Y., & Kumar, P. (2024). Indoor navigation using point cloud and digital twin technologies. Sensors, 24(3), 1502. [Google Scholar] [Crossref]

10. Chen, X., Li, Z., & Zhou, Y. (2023). AI-driven navigation systems for smart cities. IEEE Access, 11, 55321–55335. [Google Scholar] [Crossref]

11. Das, S., Roy, P., & Mukherjee, A. (2024). Digital twin architecture for smart building navigation systems. IEEE Smart Cities Conference, 1–8. [Google Scholar] [Crossref]

12. Goodchild, M. F. (2020). Geographic information systems and spatial analysis for navigation applications. International Journal of Geographical Information Science, 34(3), 1–10. [Google Scholar] [Crossref]

13. Gupta, R., Sharma, V., & Patel, D. (2023). Spatial computing frameworks for indoor navigation systems. ACM Computing Surveys, 55(6), 1–34. [Google Scholar] [Crossref]

14. Jones, D., Snider, C., Nassehi, A., et al. (2023). Characterizing digital twins in smart buildings. Automation in Construction, 145, 104567. [Google Scholar] [Crossref]

15. Khan, A., Hussain, S., & Lee, J. (2022). Machine learning techniques for indoor positioning systems. Sensors, 22(4), 1210. [Google Scholar] [Crossref]

16. Kim, J., Park, H., & Lee, S. (2023). Hybrid indoor-outdoor navigation framework for smart environments. IEEE Internet of Things Journal, 10(5), 4120–4132. [Google Scholar] [Crossref]

17. Kumar, R., Singh, A., & Verma, N. (2024). Augmented reality navigation for retail environments. Computers & Graphics, 113, 98–108. [Google Scholar] [Crossref]

18. Li, Y., Wang, X., & Sun, J. (2022). 5G-based positioning technologies for next-generation navigation systems. IEEE Network, 36(4), 120–126. [Google Scholar] [Crossref]

19. Liu, H., Darabi, H., Banerjee, P., & Liu, J. (2023). Survey of wireless indoor positioning techniques. IEEE Systems Journal, 17(2), 2018–2031. [Google Scholar] [Crossref]

20. Luo, H., Zhao, Y., & Li, Q. (2022). Point cloud-based indoor mapping techniques. Remote Sensing, 14(5), 1180. [Google Scholar] [Crossref]

21. Mapbox. (2025). Navigation SDK and Routing APIs Documentation. Mapbox Inc. [Google Scholar] [Crossref]

22. Matterport. (2024). Digital Twin Technology for Indoor Mapping. Matterport Inc. [Google Scholar] [Crossref]

23. Mur-Artal, R., & Tardós, J. (2021). ORB-SLAM systems for visual navigation. IEEE Robotics and Automation Letters, 6(4), 689–696. [Google Scholar] [Crossref]

24. Nguyen, T., Le, H., & Pham, D. (2023). Deep learning approaches for indoor localization. IEEE Access, 11, 77120–77135. [Google Scholar] [Crossref]

25. Park, S., Lee, H., & Choi, J. (2022). Seamless indoor-outdoor navigation using hybrid positioning. Sensors, 22(18), 7041. [Google Scholar] [Crossref]

26. Singh, P., Kumar, R., & Sharma, A. (2022). Sensor fusion-based hybrid navigation systems. IEEE Sensors Journal, 22(15), 14780–14790. [Google Scholar] [Crossref]

27. Speicher, M., Hall, B., & Nebeling, M. (2021). What is mixed reality? A taxonomy of immersive technologies. IEEE ISMAR, 272–283. [Google Scholar] [Crossref]

28. Tao, F., Zhang, M., & Liu, Y. (2021). Digital twin-driven smart manufacturing. IEEE Transactions on Industrial Informatics, 17(6), 1–10. [Google Scholar] [Crossref]

29. Wang, Z., Zhang, H., & Li, J. (2021). Edge computing for location-based navigation services. IEEE Internet of Things Journal, 8(9), 7223–7232. [Google Scholar] [Crossref]

30. Xu, Y., Zhou, H., & Chen, J. (2024). Multi-sensor fusion positioning systems for mobile navigation. IEEE Sensors Journal, 24(2), 1035–1046. [Google Scholar] [Crossref]

31. Yassin, A., Nasser, Y., Awad, M., et al. (2020). Recent advances in indoor localization technologies. IEEE Communications Surveys & Tutorials, 22(2), 1–30. [Google Scholar] [Crossref]

32. Zafari, F., Gkelias, A., & Leung, K. (2022). Indoor localization systems and technologies: A survey. IEEE Communications Surveys & Tutorials, 24(1), 1–30. [Google Scholar] [Crossref]

33. Zhang, Y., Wang, L., & Chen, Q. (2022). Artificial intelligence-based route optimization. Transportation Research Part C, 138, 103610. [Google Scholar] [Crossref]

34. Zhao, H., Li, J., & Sun, Y. (2022). LiDAR-based spatial mapping for indoor environments. Remote Sensing Letters, 13(7), 1–10. [Google Scholar] [Crossref]

35. Unity Technologies. (2025). AR Foundation and Spatial Mapping Documentation. Unity Technologies. [Google Scholar] [Crossref]

• What the Desert Fathers Teach Data Scientists: Ancient Ascetic Principles for Ethical Machine-Learning Practice
• Comparative Analysis of Some Machine Learning Algorithms for the Classification of Ransomware
• Comparative Performance Analysis of Some Priority Queue Variants in Dijkstra’s Algorithm
• Transfer Learning in Detecting E-Assessment Malpractice from a Proctored Video Recordings.
• Dual-Modal Detection of Parkinson’s Disease: A Clinical Framework and Deep Learning Approach Using NeuroParkNet