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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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administrative things
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

literature
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.
student project
Student Project
  • Solve a Computer Vision Problem
    • From Hardware selection over 3D Measurement to Live Test
slide6
Goal
  • Measure 3D Geometry of Electrical Discharges

Impact Area

C4

C1

C3

C2

tasks
Tasks
  • Workpackage 1: select hardware, acquire images, segment flash

(xi, yi)

tasks1
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

tasks2
Tasks
  • Workpackage 3: correspondence & triangulation

(xi, yi)

(xj, yj)

3D

organization
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.
example
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

rules
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.
robotics
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.
robot vs human
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
robotics goals and applications
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“]
statistics
Statistics

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

statistics1
Statistics

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

statistics2
Statistics

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

statistics3
Statistics

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

statistics4
Statistics

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

what can computer vision do for robotics
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

computer vision
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.
shape from structured light
Shape from Structured Light
  • Structured Light Sensor

Figures from PRIP, TU Vienna

navigation
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.
slide34
SLAM

Mapping:

slide35
SLAM

The final map:

slide36
SLAM

Navigation:

registration
Registration
  • Registration of CAD models to scene features:

Figures from P.Wunsch: Registration of CAD-Models to Images by Iterative Inverse Perspective Matching