liu.seSearch for publications in DiVA
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Local Features for Range and Vision-Based Robotic Automation
Linköping University, Department of Electrical Engineering, Information Coding. Linköping University, The Institute of Technology.
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 [en]
Local features, object pose estimation, range data
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-57333ISBN: 978-91-7393-362-9 (print)OAI: oai:DiVA.org:liu-57333DiVA: diva2:325008
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
List of papers
1. A Local Single-Patch Feature for Pose Estimation Using the Log-Polar Transform: Revised Version
Open this publication in new window or tab >>A Local Single-Patch Feature for Pose Estimation Using the Log-Polar Transform: Revised Version
(English)Manuscript (preprint) (Other academic)
Abstract [en]

This paper presents a local image feature, based on the log-polartransform which together with the Fourier transform enables feature matching invariant to orientation and scalechanges. It is shown that this feature can be used for poseestimation of 3D objects with unknown pose, with clutteredbackground and with occlusion. The proposed method is compared to apreviously published one and the new feature is found to be about asgood or better as the old one for this task.

National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-57326 (URN)
Note
This is a revised version of http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-48200Available from: 2010-06-16 Created: 2010-06-16 Last updated: 2010-06-23Bibliographically approved
2. Increasing Pose Estimation Performance using Multi-cue Integration
Open this publication in new window or tab >>Increasing Pose Estimation Performance using Multi-cue Integration
2006 (English)In: IEEE International Conference on Robotic and Automation (ICRA), IEEE , 2006, 3760-3767 p.Conference paper, Published paper (Refereed)
Abstract [en]

We have developed a system which integrates the information output from several pose estimation algorithms and from several views of the scene. It is tested in a real setup with a robotic manipulator. It is shown that integrating pose estimates from several algorithms increases the overall performance of the pose estimation accuracy as well as the robustness as compared to using only a single algorithm. It is shown that increased robustness can be achieved by using pose estimation algorithms based on complementary features, so called algorithmic multi-cue integration (AMC). Furthermore it is also shown that increased accuracy can be achieved by integrating pose estimation results from different views of the scene, so-called temporal multi-cue integration (TMC). Temporal multi-cue integration is the most interesting aspect of this paper.

Place, publisher, year, edition, pages
IEEE, 2006
Series
Robotics and Automation, ISSN 1050-4729
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-37180 (URN)10.1109/ROBOT.2006.1642277 (DOI)33871 (Local ID)0-7803-9505-0 (ISBN)33871 (Archive number)33871 (OAI)
Conference
IEEE International Conference on Robotic and Automation (ICRA), May 15-19, Orlando, Florida, USA
Projects
VISATEC
Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2010-06-23
3. A Local Geometry-Based Descriptor for 3D Data Applied to Object Pose Estimation
Open this publication in new window or tab >>A Local Geometry-Based Descriptor for 3D Data Applied to Object Pose Estimation
2010 (English)Manuscript (preprint) (Other academic)
Abstract [en]

A local descriptor for 3D data, the scene tensor, is presentedtogether with novel applications.  It can describe multiple planarsegments in a local 3D region; for the case of up to three segments itis possible to recover the geometry of the local region in terms of thesize, position and orientation of each of the segments from thedescriptor. In the setting of range data, this property makes thedescriptor unique compared to other popular local descriptors, such asspin images or point signatures.  The estimation of the descriptor canbe based on 3D orientation tensors that, for example, can be computeddirectly from surface normals but the representation itself does notdepend on a specific estimation method and can also be applied to othertypes of 3D data, such as motion stereo. A series of experiments onboth real and synthetic range data show that the proposedrepresentation can be used as a interest point detector with highrepeatability. Further, the experiments show that, at such detectedpoints, the local geometric structure can be robustly recovered, evenin the presence of noise. Last we expand a framework for object poseestimation, based on the scene tensor and previously appliedsuccessfully on 2D image data, to work also on range data. Poseestimation from real range data shows that there are advantages oversimilar descriptors in 2D and that use of range data gives superiorperformance.

Keyword
3D analysis, local descriptor, tensor, range data
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-57328 (URN)LiTH-ISY-R-2951 (ISRN)
Note

See also the addendum which is found at http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-57329

Available from: 2010-06-16 Created: 2010-06-16 Last updated: 2014-09-22Bibliographically approved
4. Point-of-Interest Detection for Range Data
Open this publication in new window or tab >>Point-of-Interest Detection for Range Data
2008 (English)In: International Conference on Pattern Recognition (ICPR), IEEE , 2008, 1-4 p.Conference paper, Published paper (Refereed)
Abstract [en]

Point-of-interest detection is a way of reducing the amount of data that needs to be processed in a certain application and is widely used in 2D image analysis. In 2D image analysis, point-of-interest detection is usually related to extraction of local descriptors for object recognition, classification, registration or pose estimation. In analysis of range data however, some local descriptors have been published in the last decade or so, but most of them do not mention any kind of point-of-interest detection. We here show how to use an extended Harris detector on range data and discuss variants of the Harris measure. All described variants of the Harris detector for 3D should also be usable in medical image analysis, but we focus on the range data case. We do present a performance evaluation of the described variants of the Harris detector on range data.

Place, publisher, year, edition, pages
IEEE, 2008
Series
Pattern Recognition, ISSN 1051-4651
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-44928 (URN)10.1109/ICPR.2008.4761179 (DOI)78311 (Local ID)978-1-4244-2175-6 (ISBN)978-1-4244-2174-9 (ISBN)78311 (Archive number)78311 (OAI)
Conference
International Conference on Pattern Recognition (ICPR), December 8-11, Tampa, Florida, USA
Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2014-06-03
5. Local Image Descriptors for Full 6 Degree-of-Freedom Object Pose Estimation and Recognition
Open this publication in new window or tab >>Local Image Descriptors for Full 6 Degree-of-Freedom Object Pose Estimation and Recognition
2010 (English)Article in journal (Refereed) Submitted
Abstract [en]

Recent years have seen advances in the estimation of full 6 degree-of-freedom object pose from a single 2D image. These advances have often been presented as a result of, or together with, a new local image feature type. This paper examines how the pose accuracy and recognition robustness for such a system varies with choice of feature type. This is done by evaluating a full 6 degree-of-freedom pose estimation system for 17 different combinations of local descriptors and detectors. The evaluation is done on data sets with photos of challenging 3D objects with simple and complex backgrounds and varying illumination conditions. We examine the performance of the system under varying levels of object occlusion and we find that many features allow considerable object occlusion. From the experiments we can conclude that duplet features, that use pairs of interest points, improve pose estimation accuracy, compared to single point features. Interestingly, we can also show that many features previously used for recognition and wide-baseline stereo are unsuitable for pose estimation, one notable example are the affine covariant features that have been proven quite successful in other applications. The data sets and their ground truths are available on the web to allow future comparison with novel algorithms.

Keyword
bin picking, pose estimation, local features
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-57330 (URN)
Note
This is an extension of http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-44894Available from: 2010-06-16 Created: 2010-06-16 Last updated: 2015-12-10
6. Object Pose Estimation using Variants of Patch-Duplet and SIFT Descriptors
Open this publication in new window or tab >>Object Pose Estimation using Variants of Patch-Duplet and SIFT Descriptors
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.

Publisher
15 p.
Series
LiTH-ISY-R, ISSN 1400-3902 ; 2950
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-57331 (URN)LiTH-ISY-R-2950 (ISRN)
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
7. Maximally Robust Range Regions
Open this publication in new window or tab >>Maximally Robust Range Regions
2010 (English)Report (Other academic)
Abstract [en]

In this work we present a region detector, an adaptation to range data of the popular Maximally Stable Extremal Regions (MSER) region detector. We call this new detector Maximally Robust Range Regions (MRRR). We apply the new detector to real range data captured by a commercially available laser range camera. Using this data we evaluate the repeatability of the new detector and compare it to some other recently published detectors. The presented detector shows a repeatability which is better or the same as the best of the other detectors. The MRRR detector also offers additional data on the detected regions. The additional data could be crucial in applications such as registration or recognition.

Publisher
8 p.
Series
LiTH-ISY-R, ISSN 1400-3902 ; 2961
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-57332 (URN)LiTH-ISY-R-2961 (ISRN)
Available from: 2010-06-16 Created: 2010-06-16 Last updated: 2015-12-10

Open Access in DiVA

Local Features for Range and Vision-Based Robotic Automation(2141 kB)1220 downloads
File information
File name FULLTEXT01.pdfFile size 2141 kBChecksum SHA-512
feb15000d95ff3d29f28c35494f0314c440cf526cb96b07be2f59d96bdcb08c262c858c79879f9f037eccd7e663ff70d0f0e4a30668e22b4fcee6ab61117e95e
Type fulltextMimetype application/pdf
Cover(136 kB)95 downloads
File information
File name COVER01.pdfFile size 136 kBChecksum SHA-512
bd04320e138fd115d35350458df4f4d729e2e174417ffcc68653655dd1873f52dc6e30246e3159927ccc1fe0958331a03939d0731975c36c1054252962299306
Type coverMimetype application/pdf

Authority records BETA

Viksten, Fredrik

Search in DiVA

By author/editor
Viksten, Fredrik
By organisation
Information CodingThe Institute of Technology
Computer Vision and Robotics (Autonomous Systems)

Search outside of DiVA

GoogleGoogle Scholar
Total: 1220 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

isbn
urn-nbn

Altmetric score

isbn
urn-nbn
Total: 2678 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf