Advancements in Indoor Navigation: From Wi-Fi Fingerprinting to Augmented Reality Integration
This project, supervised by Dr. Gunho Sohn, focuses on enhancing indoor navigation systems using Wi-Fi as a viable alternative to GPS, which fails indoors. Key achievements include completing signal surveying software and algorithm development in Matlab, and ongoing integration into mapping applications for Android. Notable progress encompasses overlaying floor plans in Google Maps, optimizing data structures for efficiency, and employing deterministic and probabilistic approaches for improved location accuracy. Future steps involve georeferencing floor plans and integrating KML mapping data.
Advancements in Indoor Navigation: From Wi-Fi Fingerprinting to Augmented Reality Integration
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Presentation Transcript
Dr. GunhoSohn (Supervisor) Alec Mantha Maninder Gill Patrick Eagan Phillip Robbins Campnav
Recall…. • Indoor Navigation System • GPS unable to reach indoors • Wi-Fi is a good alternative • GIS Database • AutoCad file manipulation • Wi-Fi fingerprint map creation • Location Analysis • Deterministic approach for rough position • Probabilistic approach for clarity Le Dortz, et al.
Progress Since Last Presentation • Completed the Signal Surveying software. • WiFi only
Progress Cont. • Completed the Algorithm in Matlab, nearly done integrating it into the mapping application. • Currently looking into the Augmented Reality toolkit for Android.
Progress Cont. • Implemented Google Maps in the Android Device. • Overlaid rough Vari Floor Plans in Google Maps.
Selected a Data Structure • Selection of sparse matrix data structure. • Drastically cuts data base size • Reduces search time • Nearly completed porting Algorithm from Matlab to Java
Performing Statistical Tests • A 2nd order trend surface “best-fit polynomial” added to data. • The highest signal intensities are near the router.
Finger Print • Information retained from a Wi-Fi scan. • Does not need to connect to any AP’s
Future Steps • Georeference and ArcGIS conversion of floor plans. • Adaptation of KML mapping data with location service. • Integration in android app. ArcGIS Floor Plans
Discussion for this week • Selection of data structure? - Real Time Computations - Pre Sorting Data? • Weights of data? - Should weights degrade with lower decibel signals ? - Reliability and accuracy of different data sets ?
Acknowledgements • We would like to thank our mentors, supervisors and stakeholders. • We would like to thank the York University Planning Department for giving us the floor plans. • We would like to thank the Counselling & Disability Service for giving us a list of POI’s.
