Dynamic Aspects of Visual Servoing and a Framework for Real-Time 3D Vision for Robotics

Authors:
Markus Vincze;Minu Ayromlou;Stefan Chroust;Michael Zillich;Wolfgang Ponweiser;Dietmar Legenstein
Affiliations:
-;-;-;-;-;-
Venue:
Revised Papers from the International Workshop on Sensor Based Intelligent Robots
Year:
2000

Citing 7
Cited 1

Pfinder: Real-Time Tracking of the Human Body

IEEE Transactions on Pattern Analysis and Machine Intelligence
X Vision: a portable substrate for real-time vision applications

Computer Vision and Image Understanding
Incremental Focus of Attention for Robust Vision-Based Tracking

International Journal of Computer Vision
Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography

Communications of the ACM
Visual Control of Robots: High-Performance Visual Serving

Visual Control of Robots: High-Performance Visual Serving
An Integrated Framework for Robust Real-Time 3D Object Tracking

ICVS '99 Proceedings of the First International Conference on Computer Vision Systems
Improving 3D Active Visual Tracking

ICVS '99 Proceedings of the First International Conference on Computer Vision Systems

Dynamic visual servoing from sequential regions of interest acquisition

International Journal of Robotics Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

Vision-based control needs fast and robust tracking. The conditions for fast tracking are derived from studying the dynamics of the visual servoing loop. The result indicates how to build the vision system to obtain high dynamic performance of tracking. Maximum tracking velocity is obtained when running image acquisition and processing in parallel and using appropriately sized tracking windows. To achieve the second criteria, robust tracking, a model-based tracking approach is enhanced with a method of Edge Projected Integration of Cues (EPIC). EPIC uses object knowledge to select the correct feature in real-time. The object pose is calculated from the features at every tracking cycle. The components of the tracking system have been implemented in a framework called Vision for Robotics (V4R). V4R has been used within the EU-funded project RobVision to navigate a robot into a ship section using the model data from the CAD-design. The experiments show the performance of tracking in different parts of the ship mock-up.