1. SPIE'01CIRL-JHU1 Dynamic Composition of Tracking Primitives for Interactive Vision-Guided Navigation D. Burschka and G. Hager Computational Interaction and Robotics Laboratory (CIRL) Johns Hopkins University

2. SPIE'01CIRL-JHU2 Outline  Introduction Motivation – Navigation Strategies  Tracking-System Architecture Pre-Processing New Tracking Definition Feature Identification  Results  Conclusions

3. SPIE'01CIRL-JHU3 Navigation Strategies Sensor-Based Control control signals for the robot are generated directly from the visual input 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

4. SPIE'01CIRL-JHU4 Tracking Primitives Dynamic Vision (XVision) algorithms Color Tracking Pattern Tracking Disparity tracking

5. SPIE'01CIRL-JHU5 XVision as Tracking Tool Dynamic Vision (XVision) algorithms applications

6. SPIE'01CIRL-JHU6 Tracking-System Architecture

7. SPIE'01CIRL-JHU7 Dynamic Composition of Tracking Cues

8. SPIE'01CIRL-JHU8 Tracking-System Architecture

9. SPIE'01CIRL-JHU9 Segmentation in the Color Space - HSI representation of color space - Variable resolution gridding of space Intensity Hue Saturation

10. SPIE'01CIRL-JHU10 Segmentation in the Disparity Domain

11. SPIE'01CIRL-JHU11 Tracking-System Architecture

12. SPIE'01CIRL-JHU12 State Transitions in the Tracking Process

13. SPIE'01CIRL-JHU13 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

14. SPIE'01CIRL-JHU14 Tracking-System Architecture

15. SPIE'01CIRL-JHU15 Quality Value for Initial Search

16. SPIE'01CIRL-JHU16 Problem in the Disparity Domain

17. SPIE'01CIRL-JHU17 Ground Plane Suppression

18. SPIE'01CIRL-JHU18 Results Obstacle Detection

19. SPIE'01CIRL-JHU19 Results Dynamic Composition

20. SPIE'01CIRL-JHU20 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

21. SPIE'01CIRL-JHU21 Additional Information: Web: http://www.cs.jhu.edu/CIRL http://www.cs.jhu.edu/~burschka