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710.088 ROBOT VISION 2VO 1KU Matthias Rüther

710.088 ROBOT VISION 2VO 1KU Matthias Rüther. Administrative Things. VO : Tuesday 14:30-16:00 HS i11 Strongly coupled with KU!! www.icg.tu-graz.ac.at/courses Exam: Written Exam Oral Exam if Requested KU: Groups of three students

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710.088 ROBOT VISION 2VO 1KU Matthias Rüther

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  1. 710.088 ROBOT VISION2VO 1KUMatthias Rüther

  2. Administrative Things VO: Tuesday 14:30-16:00 HS i11 Strongly coupled with KU!! www.icg.tu-graz.ac.at/courses Exam: Written Exam Oral Exam if Requested KU: Groups of three students Each group does the same project Effort: ~1week per student

  3. Time Table

  4. Literature • Sciavicco, L., Siciliano, B., Modelling and Control of Robot Manipulators 2nd Ed., Springer, 2000 • Sonka M., Hlavac V., Boyle Image Processing, Analysis and Machine Vision, Chapman Hall, 1998 • Hartley R., Zissermann A., Multiple View Geometry in Computer Vision, Cambridge, 2001.

  5. Student Project • Solve a Computer Vision Problem • From Hardware selection over 3D Measurement to Live Test

  6. Goal • Measure 3D Geometry of Electrical Discharges Impact Area C4 C1 C3 C2

  7. Tasks • Workpackage 1: select hardware, acquire images, segment flash (xi, yi)

  8. Tasks • Workpackage 2: camera calibration and pose estimation Impact Area RW, TW K4 K1 K3 K2 C4 C1 R21, T21 C3 C2 R31, T31 R41, T41

  9. Tasks • Workpackage 3: correspondence & triangulation (xi, yi) (xj, yj) 3D

  10. Organization • The Project is divided in three workpackages which have to be delivered during the term: • 30.3.2007 • 1.6. 2007 • 22.6. 2007 • Each group (3 students) does all three workpackages. • The workpackages build on top of the previous ones. After submission, the workpackages are published. • Each group is allowed to use previous workpackages of any other group.

  11. Example NO Collaboration during workpackage WP1: Group 1 Group 2 … Group n YES Each group may reuse previous workpackages of other groups Group 1 Group 2 … Group n WP2: WP3: Group 1 Group 2 … Group n

  12. Rules • No collaboration between groups during a workpackage. Copying groups are removed from the KU. • Every group member is held responsible for every task in every workpackage. • Code reuse has no influence on the grade. • Each group must deliver at least two workpackages. • A “Sehr Gut” on the Project gives a 25% Bonus on the Lecture exam on 3.6.2007.

  13. Robotics • What is a robot? "A reprogrammable, multifunctional manipulator designed to move material, parts, tools, or specialized devices through various programmed motions for the performance of a variety of tasks" Robot Institute of America, 1979 … in a three-dimensional environment. • Industrial • Mostly automatic manipulation of rigid parts with well-known shape in a specially prepared environment. • Medical • Mostly semi-automatic manipulation of deformable objects in a naturally created, space limited environment. • Field Robotics • Autonomous control and navigation of a mobile vehicle in an arbitrary environment.

  14. Experimental/Industrial/Commercial Robots

  15. Industrial Robots

  16. Challenging Environments

  17. Service and Assistance

  18. FRIEND Project

  19. Robot vs Human • Human advantages: • Intelligence • Flexibility • Adaptability • Skill • Can Learn • Can Estimate • Robot Advantages: • Strength • Accuracy • Speed • Does not tire • Does repetitive tasks • Can Measure

  20. Robotics: Goals and Applications • Goal: combine robot and human abilities. • Applications: • Automation (Production) • Inspection (Quality control) • Remote Sensing (Mapping) • Man-Machine interaction („Cobot“) • Robot Companion (Physically challenged people) • See [Brady, M. et. al. (eds). „Robot Motion: Planning and Control“]

  21. Statistics Yearly installations of industrial robots, 2003-2004 and forecast for 2005-2008

  22. Statistics Estimated operational stock of industrial robots 2003-2004 and forecast for 2005-2008

  23. Statistics Number of robots per 10,000 production workers in the motor vehicle industry 2002 and 2004

  24. Statistics Service robots for professional use. Stock at the end of 2004 and projected installations in 2005-2008

  25. Statistics Service robots for personal and domestic use. Stock and value of stock at the end of 2004 and projected installations in 2005 -2008

  26. What can Computer Vision do for Robotics? • Accurate Robot-Object Positioning • Keeping Relative Position under Movement • Visualization / Teaching / Telerobotics • Performing measurements • Object Recognition (see LV „Bildverarbeitung u. Mustererkennung“, „Bildverstehen“, „AK Computer Vision“) • Registration Visual Servoing

  27. Computer Vision • What is Computer Vision? "Computer Vision describes the automatic deduction of the structure and the properties of a (possible dynamic) three-dimensional world from either a single or multiple two-dimensional images of the world" [Nalva VS, "A Guided Tour of Computer Vision"] • Measurement • Measure shape and material properties in a 3D environment. Accuracy is important. • Recognition • Cognitive systems interpret a 3D environment (object classification, categorization). Systems are allowed to fail to a certain extent (similar to humans). • Navigation • Navigation Systems orient themselves in a 3D environment. Robustness and time are important.

  28. Shape from Stereo

  29. Shape from Stereo

  30. Shape from Focus

  31. Shape from Structured Light • Structured Light Sensor Figures from PRIP, TU Vienna

  32. Shape from Shading

  33. Navigation • SLAM: Simultaneous Localization and Mapping. • Where am I on my map? • If the place is unknown, build a new map, try to merge it with the original map. • Visual Odometry: calculate the relative motion of the camera between two frames. Summing up the motion gives the camera path. Error propagation! • Visual Servoing: move to / maintain a relative position between robot end effector and an object. • Tracking: continuously measure the position of an object within the sensor coordinate frame.

  34. SLAM Mapping:

  35. SLAM The final map:

  36. SLAM Navigation:

  37. Visual Odometry

  38. Visual Servoing

  39. Tracking

  40. Registration • Registration of CAD models to scene features: Figures from P.Wunsch: Registration of CAD-Models to Images by Iterative Inverse Perspective Matching

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