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A flexible seam detection t echnique

A flexible seam detection t echnique. for robotic laser welding. (Shortened English version) Jorg Entzinger. Laser bundel. Seam to Weld. Laser Focus Lens. Camera lens. Video camera. Dichroic mirror. Laser diode. Laser Focus Lens. Presentation Structure. Introduction

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A flexible seam detection t echnique

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  1. A flexible seam detection technique for robotic laser welding (Shortened English version) Jorg Entzinger

  2. Laser bundel Seam to Weld

  3. Laser Focus Lens

  4. Camera lens Video camera Dichroic mirror Laser diode

  5. Laser Focus Lens

  6. Presentation Structure • Introduction • Lens & camera calibration • Image undistortion • Seam Detection

  7. Detect seams Track & learn seams Laser weld seams Process control Quality control Functions of the Multifunctional Welding head

  8. Specialities of this welding head • Multifunctionality All needed technology is integrated in one machine • Compactness Flexible in use for complex geometries • Lightweight For higher accuracies with the use of robots

  9. Assignment • Develop a system that can compensate for lens distortions • Develop a system to determine the exact position of the workpiece with respect to the welding head from camera images

  10. Distortion Types • Perspective distortions • Camera distortions (skew, non-squareness of pixels) • Lens distortions (radial: barrel/pincushion) • Noise (dust, bad focussing, CCD measurement noise) Normal Perspective Skew Barrel Pincushion

  11. C++ Read parameters from file Generate look-op table of pixel displacements Aquire camera image Undistort image MATLAB Make calibration pattern Take pictures Identify keypoints Sort keypoints Parameter estimation Write Params to File Program Structure

  12. Program Structure MATLAB Make calibration pattern Take pictures Identify keypoints Sort keypoints Parameter estimation Write Params to File C++ Read parameters from file Generate look-op table of pixel displacements Aquire camera image Undistort image

  13. The calibration-pattern MATLAB Make calibration pattern Take pictures Identify keypoints Sort keypoints Estimate parameters Write params to file

  14. Pictures of theCalibration-pattern MATLAB Make calibration pattern Take pictures Identify keypoints Sort keypoints Estimate parameters Write params to file

  15. Identified Keypoints MATLAB Make calibration pattern Take pictures Identify keypoints Sort keypoints Estimate parameters Write params to file

  16. Sorted Keypoints MATLAB Make calibration pattern Take pictures Identify keypoints Sort keypoints Estimate parameters Write params to file

  17. Perspective Camera Parameter estimation (1)

  18. Parameter estimation (2) Barrel Pincushion or

  19. Estimation refinement Homography was calculated without considering radial distortions  Distortions are calculated from an inaccurate homography  The estimations must be refined, all parameters are optimized at the same time

  20. Program Structure MATLAB Make calibration pattern Take pictures Identify keypoints Sort keypoints Parameter estimation Write Params to File C++ Read parameters from file Generate look-op table of pixel displacements Aquire camera image Undistort image

  21. Program Structure MATLAB Make calibration pattern Take pictures Identify keypoints Sort keypoints Parameter estimation Write Params to File C++ Read parameters from file Generate look-op table of pixel displacements Aquire camera image Undistort image

  22. Undistortion

  23. An Original (Distorted) Picture

  24. Test Result

  25. Test Result Original Undistorted

  26. Simulated Distortion Test Original Undistorted

  27. Result Original Undistorted

  28. Amout of Distortion Pixel movement in % Position on image diagonal

  29. Programma Structuur MATLAB Make calibration pattern Take pictures Identify keypoints Sort keypoints Parameter estimation Write Params to File C++ Read parameters from file Generate look-op table of pixel displacements Aquire camera image Undistort image

  30. Program Structure MATLAB Make calibration pattern Take pictures Identify keypoints Sort keypoints Parameter estimation Write Params to File C++ Read parameters from file Generate look-op table of pixel displacements Aquire camera image Undistort image Determine seam location Move Robot

  31. Changes in camera image due to a change of relative position

  32. Changes in camera image due to a change of relative position

  33. World Coördinates • How many millimeters in reality is 10 pixels in the image? • If the image moves to the right, in what direction did the robot move? • Where is the camera with respect to the welding spot?

  34. Test objects

  35. Following a seam

  36. Thank you for your attention Jorg Entzinger

  37. Rotatie Matrix

  38. Rodrigues Parameters

  39. Rotatie Matrix  Rodrigues Parameters

  40. Schatten van de Parameters (1) met

  41. Aanbevelingen • Pixel-millimeter schaling hoogte afhankelijk maken • Bepaling naad-positie minder afhankelijk maken van handmatige instellingen • Zorgen voor goede afhandeling als de naad dicht bij de kruising van de lijnen komt • Goede gebruikers-interface voor camera & lens calibration maken

  42. Camera & Lens distortions • Perspective distortion • Skew distortion • Radial distortions (barrel & pincushion) • Noise Normal Perspective Skew Barrel Pincushion

  43. Why Camera Calibration?

  44. Detectie van Lijnen

  45. Scheiden van Lijnen in het Kruis

  46. Detectie van de Lasnaad

  47. Parameters schatten Er worden subsets gemaakt van Datapunten uit 4 plaatjes, bijvoorbeeld: Subset 1 Subset 2 Subset 3 ... Dataset 1 Dataset 2 Dataset 3 Dataset 2 Dataset 4 Dataset 5 Dataset 3 Dataset 6 Dataset 7 Dataset 4 Dataset 8 Dataset 8 Voor elke subset wordt een calibration uitgevoerd MATLAB calibration patroon maken Foto’s nemen Keypoints identificeren Keypoints sorteren Parameters schatten Parameters naar bestand schrijven

  48. Parameter Schattingen Op Basis Van Meerdere Subsets RMS alfa f

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