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.

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Enhanced Techniques

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

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Hough Transform

• Used to extract geometrical object features from
  digital images

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Hough Transform (cont)

• Features are extracted by detecting maximums in
  the image
• Example geometric features encountered

Lines
Circles
Ellipses
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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
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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)

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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

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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

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Tracking system model
Designed to handle aspect changes online
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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

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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

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Object tracking with visibility determination
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Multisensory Servoing

• Redundant information is used to increase the
  performance of the servoing system as well as the
  robustness against failing sensors

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Vision Controlled Robot Model
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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