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Object Pose Estimation using Variants of Patch-Duplet and SIFT Descriptors
Linköping University, Department of Electrical Engineering, Information Coding. Linköping University, The Institute of Technology.
2010 (English)Report (Other academic)
Abstract [en]

Recent years have seen a lot of work on local descriptors. In all published comparisons or evaluations, the now quite well-known SIFT-descriptor has been one of the top performers. For the application of object pose estimation, one comparison showed a local descriptor, called the Patch-duplet, of equal or better performance than SIFT. This paper examines different properties of those two descriptors by constructing and evaluating hybrids of them. We also extend upon the object pose estimation experiments of the original Patch-duplet paper. All tests use real images. We also show what impact camera calibration and image rectification has on an application such as object pose estimation. A new feature based on the Patch-duplet descriptor and the DoG detector emerges as the feature of choice under illuminiation changes in a real world application.

Place, publisher, year, edition, pages
2010. , 15 p.
Series
LiTH-ISY-R, ISSN 1400-3902 ; 2950
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-57331ISRN: LiTH-ISY-R-2950OAI: oai:DiVA.org:liu-57331DiVA: diva2:325005
Note
This is an extension of work found in http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-56268Available from: 2010-06-16 Created: 2010-06-16 Last updated: 2010-06-23
In thesis
1. Local Features for Range and Vision-Based Robotic Automation
Open this publication in new window or tab >>Local Features for Range and Vision-Based Robotic Automation
2010 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Robotic automation has been a part of state-of-the-art manufacturing for many decades. Robotic manipulators are used for such tasks as welding, painting, pick and place tasks etc. Robotic manipulators are quite flexible and adaptable to new tasks, but a typical robot-based production cell requires extensive specification of the robot motion and construction of tools and fixtures for material handling. This incurs a large effort both in time and monetary expenses. The task of a vision system in this setting is to simplify the control and guidance of the robot and to reduce the need for supporting material handling machinery.

This dissertation examines performance and properties of the current state-of-the-art local features within the setting of object pose estimation. This is done through an extensive set of experiments replicating various potential problems to which a vision system in a robotic cell could be subjected. The dissertation presents new local features which are shown to increase the performance of object pose estimation. A new local descriptor details how to use log-polar sampled image patches for truly rotational invariant matching. This representation is also extended to use a scale-space interest point detector which in turn makes it very competitive in our experiments. A number of variations of already available descriptors are constructed resulting in new and competitive features, among them a scale-space based Patch-duplet.

In this dissertation a successful vision-based object pose estimation system is extended for multi-cue integration, yielding increased robustness and accuracy. Robustness is increased through algorithmic multi-cue integration, combining the individual strengths of multiple local features. Increased accuracy is achieved by utilizing manipulator movement and applying temporal multi-cue integration. This is implemented using a real flexible robotic manipulator arm.

Besides work done on local features for ordinary image data a number of local features for range data has also been developed. This dissertation describes the theory behind and the application of the scene tensor to the problem of object pose estimation. The scene tensor is a fourth order tensor representation using projective geometry. It is shown how to use the scene tensor as a detector as well as how to apply it to the task of object pose estimation. The object pose estimation system is extended to work with 3D data.

A novel way of handling sampling of range data when constructing a detector is discussed. A volume rasterization method is presented and the classic Harris detector is adapted to it. Finally, a novel region detector, called Maximally Robust Range Regions, is presented. All developed detectors are compared in a detector repeatability test.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2010. 81 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1325
Keyword
Local features, object pose estimation, range data
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-57333 (URN)978-91-7393-362-9 (ISBN)
Public defence
2010-09-09, Glashuset, House B, entrance 25,, Campus Valla, Linköpings universitet, Linköping, 13:00 (English)
Opponent
Supervisors
Available from: 2010-06-17 Created: 2010-06-17 Last updated: 2010-06-23Bibliographically approved

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Viksten, Fredrik

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Citation style
  • apa
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  • rtf