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Dynamic Composition of Tracking Primitives for Interactive Vision-Guided Navigation

Dynamic Composition of Tracking Primitives for Interactive Vision-Guided Navigation. D. Burschka and G. Hager C omputational I nteraction and R obotics L aboratory (CIRL) Johns Hopkins University. Outline. Introduction Motivation – Navigation Strategies Tracking-System Architecture

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Dynamic Composition of Tracking Primitives for Interactive Vision-Guided Navigation

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  1. Dynamic Composition of Tracking Primitives for Interactive Vision-Guided Navigation D. Burschka and G. Hager Computational Interaction and Robotics Laboratory (CIRL) Johns Hopkins University CIRL-JHU

  2. Outline • Introduction • Motivation – Navigation Strategies • Tracking-System Architecture • Pre-Processing • New Tracking Definition • Feature Identification • Results • Conclusions CIRL-JHU

  3. Navigation Strategies Map-Based Navigation pre-processed sensor data is stored in a geometrical representation of the envi- ronment (map). Path plan- ning+strategy algorithms are used to define the actions of the robot Sensor-Based Control control signals for the robot are generated directly from the visual input CIRL-JHU

  4. Tracking Primitives Disparity tracking Pattern Tracking Color Tracking Dynamic Vision (XVision) algorithms CIRL-JHU

  5. XVision as Tracking Tool Dynamic Vision (XVision) algorithms applications CIRL-JHU

  6. Tracking-System Architecture CIRL-JHU

  7. Dynamic Composition of Tracking Cues CIRL-JHU

  8. Tracking-System Architecture CIRL-JHU

  9. Segmentation in the ColorSpace Hue Saturation Intensity - HSI representation of color space - Variable resolution gridding of space CIRL-JHU

  10. Segmentation in the Disparity Domain CIRL-JHU

  11. Tracking-System Architecture CIRL-JHU

  12. State Transitions in the Tracking Process CIRL-JHU

  13. State Information saved in the Tracking Module • Information about the object in the real scene is shared between the different Image Identifications: • Position in the image • Size of the region • Range in the current image domain • Shape ratio in the image • Compactness of the region CIRL-JHU

  14. Tracking-System Architecture CIRL-JHU

  15. Quality Value for Initial Search CIRL-JHU

  16. Problem in the Disparity Domain CIRL-JHU

  17. Ground Plane Suppression CIRL-JHU

  18. Results Obstacle Detection CIRL-JHU

  19. Results Dynamic Composition CIRL-JHU

  20. Conclusions and Future Work: • Dynamic Composition of the two Basic Feature Identification tools allowed robust initial selection and navigation through a door • Extension to the entire set of Feature Identification tools is our next step • The developed algorithms allow robust obstacle avoidance CIRL-JHU

  21. Additional Information: Web: http://www.cs.jhu.edu/CIRL http://www.cs.jhu.edu/~burschka CIRL-JHU

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