1. Position and Attitude Determination using Digital Image Processing Sean VandenAvond Mentors: Brian Taylor, Dr. Demoz Gebre-Egziabher A UROP sponsored research project

2. Overview Motivation Introduction Feature Matching Position Determination Google Earth Navigation Simulation Future Work

3. Inertial Measurement Units Measure linear accelerations and rotational velocities May contain bias error Error grows over time $1,000 – $100,000

4. GPS Update IMU Readings Integrate Attitude and Position GPS Update Error or Noise IMU frequency: 50-200 hz GPS frequency: 1 hz

5. Problems with GPS GPS failure can occur in small UAVs -Interference -Jamming -Spoofing University of Texas successfully spoofed an UAV GPS (2012) and an 80 million dollar yacht (2013). RQ-170 Sentinel speculated to be spoofed and captured by Iranians (2011) Severely limits autonomy and control without backup system.

6. Motivation Small UAVs are limited by: –Price –Weight A backup system is required to increase the reliability. This system should be: –Inexpensive –Lightweight –Self-enclosed –Robust and practical

7. GPS Images Position Update Processing

8. Proof of Concept MATLAB –OpenSURF –Algorithms Google Earth –KML files Feature Matching Navigation Algorithms Simulation

9. Feature Matching Feature: unique grouping of pixels in an image. Given two images the program will output best estimates on matching features. Limitations: -shadows/lighting -lack of unique image data -minimal relative image rotation These limitations can lead to mismatched feature points between images  error in position estimations.

10. Feature Matching OpenSURF

11. Limitations Shadows and lighting can degrade image matching.

12. Limitations No good feature points

13. Limitations Pitch = 40º Heading = 40º

14. Position and Attitude Determination PnP problem Uses known landmark positions –At least 3 landmarks Various PnP algorithms available. – Direct Linear Transform –EPnP –Constrained Least Square positions.

15. Depth Ambiguity Information lost when converting from 3D scene to 2D image. Need to know landmark coordinates in all three dimensions. Non unique solution if you don’t have 3 rd dimension Algorithms breaks down when viewing 2D scene

16. Limitations

18. Simpler Solution Let Heading = Pitch = Roll = 0º

19. Position Determination Use image matching and FOV calculations to determine new position.

20. Resulting Position

21. Attitude Error

22. Simulation Full guidance and navigation simulation in Google Earth using MATLAB Assumptions: -Heading = Pitch = Roll = 0 -Neglecting aircraft dynamics -Fixed velocity -Fixed altitude

23. Initial image at known lat/lon Destination New heading “Aircraft Simulation” New image Feature matching Estimate new lat/lon Old image

24. Results

25. Matched Images

26. Mismatch Error

27. Percent Overlap Image 1 Image 2 Overlap More Overlap: -Better matches -Slower Tests done with 3000 foot length step  overlap of about 25 percent

28. Summary Over 100 miles in simulation. Show promise for using digital image processing for a backup navigation system. Benefits -Lightweight -Inexpensive -Self-enclosed Limitations -Needs unique feature points -Computationally expensive

29. Future Work Couple simulation with UAV lab MATLAB script to include errors in sensors. Post-processing from UAV lab flight tests Adapt to allow onboard flight testing

30. Results