1. Stereoscopic Imaging for Slow-Moving Autonomous Vehicle By: Alexander Norton Advisor: Dr. Huggins April 26, 2012 Senior Capstone Project Final Presentation Bradley University ECE Department

2. Presentation Outline Project Overview Stereoscopic Imaging Overview Previous Work Functional and System Description Completed Work Results Suggestions for Future Work

3. Project Overview The goal of this project was to design a stereoscopic imaging system using two low cost digital cameras that could calculate depth information from sets of images which could then be used to navigate an autonomous vehicle Two modes of operation: calibration mode and run mode

4. Stereoscopic Imaging Overview The use of two horizontally aligned cameras separated by a fixed distance that take a pair of images at the same time Calibrate cameras so they act like pin hole cameras Determine corresponding pixel groups Find the disparity (offset in the x coordinate) between the corresponding pixel groups. Use triangulation to find distance to pixel groups This depth information can be used to create a 3-D terrain map

5. Previous Work BirdTrak (Brian Crombie and Matt Zivney, 2003) Bradley Rover(Steve Goggins, Rob Scherbinski, Pete Lange, 2005) NavBot (Adam Beach, Nick Wlaznik, 2007) SVAN (John Hessling, 2010)

6. System Description System block diagram Subsystem block diagrams  Cameras  Computer  Software Modes of operation  Calibration mode  Run mode

7. System Block Diagram

8. Cameras Subsystem

9. Computer Subsystem

10. Calibration Mode

11. Run Mode

12. Necessity of Calibration Produces the rotation and translation matrices needed to rectify sets of images Rectification makes the stereo correspondence more accurate and more efficient Failing to calibrate the cameras is the reason for why past groups have failed to get accurate results and useful system.

13. Completed Work Calibration mode software Input is a list of sets of images of a chessboard, and the number of corners along the length and width of the chessboard Read in the left and right image pairs, find the chessboard corners, and set object and image points for the images where all the chessboards could be found Given this list of determined points on the chessboard images, the code calls stereoCalibrate() to calibrate the cameras

14. Calibration Mode Software This calibration yields the camera matrix M and the distortion vector D for the two cameras; it also yields the rotation matrix R, the translation vector T, the essential matrix E, and the fundamental matrix F The accuracy of the calibration is assessed by the software using “epipolar” geometry.

15. Calibration Mode Software The code then moves on to computing the rectification maps using stereoRectify() The rectification maps are used when processing sets of images obtained in run mode

16. Calibration Mode Software Matrices Rotation matrix R, Translation Vector T : extrinsic matrices, put the right camera in the same plane as the left camera, which makes the two image planes coplanar Fundamental matrix F: intrinsic matrix, relates the points on the image plane of one camera in pixels to the points on the image plane of the other camera in pixels

17. Calibration Mode Software Matrices Essential Matrix E: intrinsic matrix, relates the physical location of the point P as seen by the left camera to the location of the same point as seen by the right camera Camera matrix M, distortion matrix D: intrinsic matrices, calculated and used within the function

18. Completed Work Run Mode Software Uses the matrices obtained from calibration Rectifies each set of images to correct for distortions Computes and displays the disparity map

19. Calibration Mode Results Output showing found chessboard corners

20. Calibration Mode Results Output rectified chessboard images

21. Calibration Mode Results Command window showing calibration results

22. Run Mode Results Output rectified set of images after cameras have been calibrated

23. Run Mode Results Output disparity map of rectified set of images

24. Theoretical Run Mode Results One image from a set of sample images Disparity map obtained from the set of sample images

25. Results Wrote working code using OpenCV libraries and functions Successfully grab images Some outputs of calibration are correct Unable to accurately compute the disparity map of an image with a simple target in front of a plain background.

26. Possible Errors Incorrect calibration results Cameras could have internal flaws that cannot be corrected with sufficient accuracy. Correspondence calculation could have errors.

27. Suggestions for Future Work Investigate the mathematics underlying the OpenCV functions Develop methods to find and correct for errors that occur as a result of incorrect calibrations and/or correspondence computations.

28. Questions??