1. Stereoscopic Imaging for Slow-Moving Autonomous Vehicle By: Alex Norton Advisor: Dr. Huggins February 28, 2012 Senior Project Progress Report Bradley University ECE Department

2. Presentation Outline Review of Proposed Project  Project Overview  Original Proposed Schedule Tasks Completed  Webcams setup  Calibration mode software Remaining Tasks  Run mode software  Improve existing software Revised Schedule

3. Project Overview Two horizontally aligned, slightly offset cameras taking a pair of images at the same time By matching corresponding pixels between the two images, the distances to objects can be calculated using triangulation This depth information can be used to create a 3-D image and terrain map

4. Original Proposed Schedule Tentative Schedule for Spring 2012 WeeksAlex NortonMatthew Foster 1Assemble camera setup 2Configure calibration rigEnsure OpenCV runs correctly on lab computers 3Begin writing OpenCV code for calibration mode Begin writing OpenCV code for run mode 4Continue writing OpenCV code for calibration mode Continue writing OpenCV code for run mode 5Continue writing OpenCV code for calibration mode Continue writing OpenCV code for run mode 6Continue writing OpenCV code for calibration mode Continue writing OpenCV code for run mode 7Test and debug calibration mode codeContinue writing OpenCV code for run mode 8Test and debug calibration mode codeContinue writing OpenCV code for run mode 9Test run mode code with calibrated cameras 10Debug calibration mode codeDebug run mode code 11Debug calibration mode codeDebug run mode code 12Test and debug complete computer vision code 13Test and debug complete computer vision code 14Prepare for final presentation

5. Tasks Completed Webcams setup Creates “capture” objects for both webcams Takes a set of images each time the “enter” key is pressed Displays the saved set of images in two windows Saves the images to a specified folder to use for further image processing

6. Webcams Setup Output

7. 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 a possible reason for why past groups have failed to get accurate results

8. 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 found points on the chessboard images, the code calls cvStereoCalibrate() to calibrate the cameras

9. Calibration Mode Software This calibration gives us 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 checking how nearly the points in one image lie on the epipolar lines of the other image

10. Calibration Mode Software The code then moves on to computing the rectification maps using Bouguet’s method with cvStereoRectify() The rectified images are then computed using cvRemap() The disparity map is then computed by using cvFindStereoCorrespondenceBM()

11. Calibration Mode Software Two methods for stereo rectification  Hartley’s Method: uses the fundamental matrix, does not require the cameras to be calibrated, produces more distorted images than Bouguet’s method  Bouget’s Method: uses the rotation and translation parameters from two calibrated cameras, also outputs the reprojection matrix Q used to project two dimensional points into three dimensions

12. 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

13. 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

14. Calibration Mode Software Example of bad chessboard image

15. Calibration Mode Software Output when bad chessboard images are run through the calibration software

16. Calibration Mode Software Example of good chessboard image

17. Calibration Mode Software Output when good chessboard images are run through the calibration software

18. Remaining Tasks Use triangulation to determine distances to objects Calculate the error in the distance measurements Minimize the error in both the camera calibration and the distance measurements

19. Revised Schedule Schedule for Spring 2012 WeeksAlex NortonMatthew Foster 7Test and debug calibration mode code 8 9 Write OpenCV code for run mode 10 Write OpenCV code for run mode 11 Test and debug run mode code 12 Test and debug run mode code 13 Test and debug complete computer vision code 14 Test and debug complete computer vision code, prepare for final presentation

20. Questions??