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A Local Geometry-Based Descriptor for 3D Data Applied to Object Pose Estimation
Linköping University, Department of Electrical Engineering, Information Coding. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
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.

Place, publisher, year, edition, pages
Keyword [en]
3D analysis, local descriptor, tensor, range data
National Category
Computer Vision and Robotics (Autonomous Systems)
URN: urn:nbn:se:liu:diva-57328ISRN: LiTH-ISY-R-2951OAI: diva2:324997

See also the addendum which is found at

Available from: 2010-06-16 Created: 2010-06-16 Last updated: 2014-09-22Bibliographically approved
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.
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1325
Local features, object pose estimation, range data
National Category
Computer Vision and Robotics (Autonomous Systems)
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)
Available from: 2010-06-17 Created: 2010-06-17 Last updated: 2010-06-23Bibliographically approved

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