Position and Attitude Determination using Digital Image Processing

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

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 Erroror Noise IMU Readings GPS Update Attitude and Position Integrate • 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. Position Update GPS Images Processing

8. Proof of Concept MATLAB • OpenSURF • Algorithms Google Earth • KML files

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 3rd dimension • Algorithms breaks down when viewing 2D scene

16. Limitations

17. 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 • Pitch of 5 degrees: estimated position error of

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

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