Photometric Visual Servoing

This paper proposes a new way to achieve robotic tasks by two-dimensional (2-D) visual servoing. Indeed, instead of using classical geometric features such as points, straight lines, pose, or a homography, as is usually done, the luminance of all pixels in the image is considered here. The main adva...

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Bibliographic Details
Published inIEEE transactions on robotics Vol. 27; no. 4; pp. 828 - 834
Main Authors Collewet, C., Marchand, E.
Format Journal Article
LanguageEnglish
Published New York, NY IEEE 01.08.2011
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Adaptative systems
Applied sciences
Artificial intelligence
Automatic Control Engineering
Cameras
Computation
Computational modeling
Computer Science
Computer science; control theory; systems
Control theory. Systems
Cost function
Exact sciences and technology
Kinematics
Lighting
Luminance
Matrix
optimization
Pattern recognition. Digital image processing. Computational geometry
photometry
Robotics
Robust control
Straight lines
Tasks
Tracking
Vision systems
Visual
visual features
Visual servoing
Visualization
Occlusion
Positioning
Tracking
Tracking task
Visual servoing
Modeling
Optimization
Robotics
Luminance
Homograph
Tracking(movable target)
visual features
Illumination
Cost function
Robustness
Occultation
Photometry
VISUAL SERVOING, ROBOTICS
LUMINANCE, LUMINANCE
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Summary:This paper proposes a new way to achieve robotic tasks by two-dimensional (2-D) visual servoing. Indeed, instead of using classical geometric features such as points, straight lines, pose, or a homography, as is usually done, the luminance of all pixels in the image is considered here. The main advantage of this new approach is that it requires no tracking or matching process. The key point of our approach relies on the analytic computation of the interaction matrix. This computation is based either on a temporal luminance-constancy hypothesis or on a reflection model so that complex illumination changes can be considered. Experimental results on positioning and tracking tasks validate the proposed approach and show its robustness to approximated depths, low-textured objects, partial occlusions, and specular scenes. They also showed that luminance leads to lower positioning errors than a classical visual servoing based on 2-D geometric visual features.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:1552-3098
1941-0468
DOI:10.1109/TRO.2011.2112593