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Methods for Visually Guided Robotic Systems: Matching, Tracking and Servoing
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
2009 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis deals with three topics; Bayesian tracking, shape matching and visual servoing. These topics are bound together by the goal of visual control of robotic systems. The work leading to this thesis was conducted within two European projects, COSPAL and DIPLECS, both with the stated goal of developing artificial cognitive systems. Thus, the ultimate goal of my research is to contribute to the development of artificial cognitive systems.

The contribution to the field of Bayesian tracking is in the form of a framework called Channel Based Tracking (CBT). CBT has been proven to perform competitively with particle filter based approaches but with the added advantage of not having to specify the observation or system models. CBT uses channel representation and correspondence free learning in order to acquire the observation and system models from unordered sets of observations and states. We demonstrate how this has been used for tracking cars in the presence of clutter and noise.

The shape matching part of this thesis presents a new way to match Fourier Descriptors (FDs). We show that it is possible to take rotation and index shift into account while matching FDs without explicitly de-rotate the contours or neglecting the phase. We also propose to use FDs for matching locally extracted shapes in contrast to the traditional way of using FDs to match the global outline of an object. We have in this context evaluated our matching scheme against the popular Affine Invariant FDs and shown that our method is clearly superior.

In the visual servoing part we present a visual servoing method that is based on an action precedes perception approach. By applying random action with a system, e.g. a robotic arm, it is possible to learn a mapping between action space and percept space. In experiments we show that it is possible to achieve high precision positioning of a robotic arm without knowing beforehand how the robotic arm looks like or how it is controlled.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press , 2009. , 93 p.
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1416
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-51657Local ID: LIU-TEK-LIC-2009:24ISBN: 978-91-7393-527-2 (print)OAI: oai:DiVA.org:liu-51657DiVA: diva2:278320
Presentation
2009-12-18, Glashuset, Hus B, Campus Valla, Linköpings universitet, Linköping, 13:15 (English)
Opponent
Supervisors
Available from: 2009-11-25 Created: 2009-11-12 Last updated: 2016-05-04Bibliographically approved
List of papers
1. Patch Contour Matching by Correlating Fourier Descriptors
Open this publication in new window or tab >>Patch Contour Matching by Correlating Fourier Descriptors
2009 (English)In: Digital Image Computing: Techniques and Applications (DICTA), IEEE Computer Society , 2009, 40-46 p.Conference paper, Published paper (Refereed)
Abstract [en]

Fourier descriptors (FDs) is a classical but still popular method for contour matching. The key idea is to apply the Fourier transform to a periodic representation of the contour, which results in a shape descriptor in the frequency domain. Fourier descriptors have mostly been used to compare object silhouettes and object contours; we instead use this well established machinery to describe local regions to be used in an object recognition framework. We extract local regions using the Maximally Stable Extremal Regions (MSER) detector and represent the external contour by FDs. Many approaches to matching FDs are based on the magnitude of each FD component, thus ignoring the information contained in the phase. Keeping the phase information requires us to take into account the global rotation of the contour and shifting of the contour samples. We show that the sum-of-squared differences of FDs can be computed without explicitly de-rotating the contours. We compare our correlation based matching against affine-invariant Fourier descriptors (AFDs) and demonstrate that our correlation based approach outperforms AFDs on real world data.

Place, publisher, year, edition, pages
IEEE Computer Society, 2009
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-51656 (URN)10.1109/DICTA.2009.17 (DOI)978-0-7695-3866-2 (ISBN)
Conference
2009 Digital Image Computing: Techniques and Applications, December 01- 03, Melbourne, Australia
Note
©2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Fredrik Larsson, Michael Felsberg and Per-Erik Forssén, Patch Contour Matching by Correlating Fourier Descriptors, 2009, Digital Image Computing: Techniquesand Applications (DICTA), Melbourne, Australia, December 2009. IEEE Computer Society. Available from: 2009-11-12 Created: 2009-11-12 Last updated: 2016-05-04Bibliographically approved
2. Learning Higher-Order Markov Models for ObjectTracking in Image Sequences
Open this publication in new window or tab >>Learning Higher-Order Markov Models for ObjectTracking in Image Sequences
2009 (English)In: Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II, Berlin, Heidelberg: Springer-Verlag , 2009, 184-195 p.Conference paper, Published paper (Refereed)
Abstract [en]

This work presents a novel object tracking approach, where the motion model is learned from sets of frame-wise detections with unknown associations. We employ a higher-order Markov model on position space instead of a first-order Markov model on a high-dimensional state-space of object dynamics. Compared to the latter, our approach allows the use of marginal rather than joint distributions, which results in a significant reduction of computation complexity. Densities are represented using a grid-based approach, where the rectangular windows are replaced with estimated smooth Parzen windows sampled at the grid points. This method performs as accurately as particle filter methods with the additional advantage that the prediction and update steps can be learned from empirical data. Our method is compared against standard techniques on image sequences obtained from an RC car following scenario. We show that our approach performs best in most of the sequences. Other potential applications are surveillance from cheap or uncalibrated cameras and image sequence analysis.

Place, publisher, year, edition, pages
Berlin, Heidelberg: Springer-Verlag, 2009
Series
Lecture Notes in Computer Science, ISSN 0302-9743 (print), 1611-3349 (online) ; 5876
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-50495 (URN)10.1007/978-3-642-10520-3_17 (DOI)000279247100017 ()978-3-642-10519-7 (ISBN)
Conference
The 5th International Symposium on Advances in Visual Computing (ISVC), Las Vegas, USA, December
Projects
DIPLECS
Available from: 2009-10-12 Created: 2009-10-12 Last updated: 2016-05-04Bibliographically approved
3. Simultaneously learning to recognize and control a low-cost robotic arm
Open this publication in new window or tab >>Simultaneously learning to recognize and control a low-cost robotic arm
2009 (English)In: Image and Vision Computing, ISSN 0262-8856, Vol. 27, no 11, 1729-1739 p.Article in journal (Refereed) Published
Abstract [en]

In this paper, we present a visual servoing method based on a learned mapping between feature space and control space. Using a suitable recognition algorithm, we present and evaluate a complete method that simultaneously learns the appearance and control of a low-cost robotic arm. The recognition part is trained using an action precedes perception approach. The novelty of this paper, apart from the visual servoing method per se, is the combination of visual servoing with gripper recognition. We show that we can achieve high precision positioning without knowing in advance what the robotic arm looks like or how it is controlled.

Keyword
Gripper recognition; Jacobian estimation; LWPR; Visual servoing
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-21195 (URN)10.1016/j.imavis.2009.04.003 (DOI)
Note
Original Publication: Fredrik Larsson, Erik Jonsson and Michael Felsberg, Simultaneously learning to recognize and control a low-cost robotic arm, 2009, Image and Vision Computing, (27), 11, 1729-1739. http://dx.doi.org/10.1016/j.imavis.2009.04.003 Copyright: Elsevier Science B.V., Amsterdam. http://www.elsevier.com/ Available from: 2009-09-30 Created: 2009-09-30 Last updated: 2016-05-04Bibliographically approved

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