This document summarizes a project to develop an indoor navigation application called "Inigator" that uses image matching to locate a user and guide them to their destination on a map. Key aspects discussed include using a database of images and locations, finding the shortest path for navigation, challenges of image matching and scaling, and plans for future improvements to the system such as automatic image collection and outdoor GPS.

1. Supervisor:
Dr. Mikhail Mikhail
Advisor:
Dr. Mohamed Moustafa
Randa
Ewada
Samah
Ahmed
Ali
El-Sheikh
Mena
Zaki
Ahmed
El-Hassafy
Nermine
Abdel-Salam
Hi, I’m Inigator
I Never Get Lost!

2. In the End ,We Tend To
Remember the Beginnings…
Inspiration:

3. Quick Scenario

4. Problem to solve?
 I just can’t get there…

5. Tell me the class
you’re in & I’ll come
get you!

6. Problem?
 I don’t even know where am I?

7. How about a photo?

8. . .
Problem?

9. Now I’ll just go
through my album
and find you!

10. How about a photo?
SSE Building,
4th row, first column,
Ah! That’s to the right
of the architecture labs

11. How we actually did it?

12. Map Generation

13. To navigate, we need
 For each image, we give it exact location
 Created a database of all the names of the images and
their locations (so once user get a match, we find
his/her exact location
 Navigate user from the detected current location to
destination

14. Navigation
 Find shortest path
 Show path for the user to follow

15. Tracking

16. Train the computer to be smart
Collect images
Extract interest points
Make Inverted file index
(each interest point exist in
which images)

17. Give it an image to match
Extract interest points in the
query image
For each interest point
increase counter of existing
images
Image with the max number
is our first best match

18. Interface

19. Interface: Options

20. Interface: Choice Was To Take
a Picture
1 2 3 4

21. Interface: Choice Was To
Enter Class/office Number
1 2 3

22. Challenges Faced
 Different images in terms of illumination, scaling, and
occlusion
 Accuracy (Trade off between number of interest points
and speed)
 Scaling (mapping)
 Set exact location of an image
 integration between process of image matching and
navigation
 offline vs online files reading (use a server )

23. Future Work
 Automatize
 Adding images by the users to the server and adding the
nearest node for each image
 Adding maps to the server and importing them in the
application
 Looking at several possible paths at once
 Dynamic map generation
 Dynamic image collection
 Gps switch mode when outdoors

24. Demo

25. Accuracy

26. What we promised to deliver:
 Robust image matcher
 Indoor navigation app
 Hardware independent application

27. What we delivered
 Robust image matcher
 Indoor navigation app
 Hardware independent application

28. Reach the things
that you can’t see!
Thank you for your attention
Questions?