SPIE'01CIRL-JHU1 Dynamic Composition of Tracking Primitives for Interactive Vision-Guided Navigation D. Burschka and G. Hager Computational Interaction.

SPIE'01 CIRL-JHU 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

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Outline

Introduction Motivation – Navigation Strategies

Tracking-System Architecture Pre-Processing New Tracking Definition Feature Identification

Results Conclusions

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Navigation Strategies

Sensor-Based Control control signals for the robot are generated directly from the visual input

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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

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Tracking Primitives

Dynamic Vision(XVision)

algorithms

Color Tracking Pattern Tracking Disparity tracking

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XVision as Tracking Tool

Dynamic Vision(XVision)

algorithms

applications

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Tracking-System Architecture

Templates(SSD)

Hue(Color Blob)

Disparity(Disparity Region)

Points Curves

Feature Extraction

Feature-Based

Region-Based

Domain Conversion

Tracking Module

User/Task

Physical Hardware-Layer

Image Processing-Layer

Tracking-Layer

Coordination-Layer

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Dynamic Composition of Tracking Cues

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Tracking-System Architecture

Templates(SSD)

Hue(Color Blob)

Disparity(Disparity Region)

Points Curves

Feature Extraction

Feature-Based

Region-Based

Domain Conversion

Tracking Module

User/Task

Physical Hardware-Layer

Image Processing-Layer

Tracking-Layer

Coordination-Layer

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Segmentation in the ColorSpace

- HSI representation of color space

- Variable resolution gridding of space

Intensity

Hue

Saturation

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Segmentation in the Disparity Domain

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Tracking-System Architecture

Templates(SSD)

Hue(Color Blob)

Disparity(Disparity Region)

Points Curves

Feature Extraction

Feature-Based

Region-Based

Domain Conversion

Tracking Module

User/Task

Physical Hardware-Layer

Image Processing-Layer

Tracking-Layer

Coordination-Layer

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State Transitions in the Tracking Process

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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 imageSize of the regionRange in the current image domainShape ratio in the imageCompactness of the region

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Tracking-System Architecture

Templates(SSD)

Hue(Color Blob)

Disparity(Disparity Region)

Points Curves

Feature Extraction

Feature-Based

Region-Based

Domain Conversion

Tracking Module

User/Task

Physical Hardware-Layer

Image Processing-Layer

Tracking-Layer

Coordination-Layer

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Quality Value for Initial Search

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A

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Problem in the Disparity Domain

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Ground Plane Suppression

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Results Obstacle Detection

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Results Dynamic Composition

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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

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Additional Information:

Web: http://www.cs.jhu.edu/CIRL http://www.cs.jhu.edu/~burschka