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Vision Based Control Motion

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Vision Based Control Motion Matt Baker Kevin VanDyke – PowerPoint PPT presentation

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Title: Vision Based Control Motion


1
Vision Based Control Motion
  • Matt Baker
  • Kevin VanDyke

2
Robots
  • Todays robots perform complex tasks with amazing
    precision and speed
  • Why then have they not moved from the structure
    of the factory floor into the real world? What
    is the limiting factor?

3
A Seeing Robot
  • A robot that can perceive and react in complex
    and unpredictable surroundings
  • This is not possible with the marker-based
    systems in use in most laboratory vision-based
    control systems

4
Common reasons for failure of vision systems
  • Small changes in the environment can result in
    significant variations in image data
  • Changes in contrast
  • Unexpected occlusion of features

5
Robustness
  • Stable measurements of local feature attributes,
    despite significant changes in the image data,
    that result from small changes in the 3D
    environment 1.

6
Enhanced Techniques
  • The Hough-Transform
  • Robust color classification
  • Occlusion prediction
  • Multisensory visual servoing

7
Hough Transform
  • Used to extract geometrical object features from
    digital images

8
Hough Transform (cont)
  • Features are extracted by detecting maximums in
    the image
  • Example geometric features encountered

Lines
Circles
Ellipses
9
Hough Transform (contd)
  • Advantages
  • Noise and background clutter do not impair
    detection of local maxima
  • Partial occlusion and varying contrast are
    minimized
  • Negatives
  • Requires time and space storage that increases
    exponentially with the dimensions of the
    parameter space

10
Hough Transform (cont)
  • a real-time application of HT requires both a
    fast image preprocessing step and an efficient
    implementation

Implementation of a circle tracking algorithm
based on HT
11
Robust color classification
  • Color has high disambiguity power
  • Real-time is required
  • Supervised color segmentation
  • The color distribution of the current scene is
    analyzed and colors that do not appear in the
    scene are used as marker colors
  • These markers are then used as the input to the
    visual servoing system
  • Colors represented by their hue-saturation value
    (HS relate to color, V relates to brightness)

12
Robust color classification (cont)
  • Color segmentation
  • Choose four colors as marker colors
  • Color markers brought onto object we wish to
    track
  • markers outlined
  • Color distribution computed
  • Initial segmentation

13
Model-based handling of occlusion
  • The previous two techniques take care of bad
    illumination and partial occlusion
  • What about aspect changes (complete occlusion)?
  • Build and maintain a 3D model of the observed
    objects so they can be tracked despite occlusion
  • Then use prediction

14
Tracking system model
Designed to handle aspect changes online
15
Prediction
  • Extract measurements of object features based on
    raw sensor data
  • Estimate the spatial position and orientation of
    the target object
  • Based on history of estimated poses and
    assumptions about the object motion you can
    predict an object pose expected in next sampling
    interval
  • With predicted pose and 3D model we are able to
    determine feature visibility in advance
  • Guide the feature extraction process for the next
    frame without the risk of searching for occluded
    features

16
Model-based handling of occlusion (cont)
  • Efficient Hidden Line Removal
  • Explicit modeling of curved object structures
    allows us to remove virtual lines or lines that
    do not have a physical correspondence in the
    camera image

17
Object tracking with visibility determination
18
Multisensory Servoing
  • Redundant information is used to increase the
    performance of the servoing system as well as the
    robustness against failing sensors

19
Vision Controlled Robot Model
20
Conclusions
  • We explored a variety of image processing
    techniques that can significantly improve the
    robustness of visual servoing systems
  • These techniques can be implemented in modern
    robot vision control systems
  • Techniques such as these will make machine vision
    in robots a reality in the near future
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