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Improved local shape feature stability through dense model tracking
Örebro University, School of Science and Technology. (Centre for Applied Autonomous Sensor Systems ( AASS ))ORCID iD: 0000-0001-7035-5710
Örebro University, School of Science and Technology. (Centre for Applied Autonomous Sensor Systems ( AASS ))ORCID iD: 0000-0002-6013-4874
Örebro University, School of Science and Technology. (Centre for Applied Autonomous Sensor Systems ( AASS ))ORCID iD: 0000-0003-0217-9326
2013 (English)In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, 2013, 3203-3209 p.Conference paper, Published paper (Refereed)
Abstract [en]

In this work we propose a method to effectively remove noise from depth images obtained with a commodity structured light sensor. The proposed approach fuses data into a consistent frame of reference over time, thus utilizing prior depth measurements and viewpoint information in the noise removal process. The effectiveness of the approach is compared to two state of the art, single-frame denoising methods in the context of feature descriptor matching and keypoint detection stability. To make more general statements about the effect of noise removal in these applications, we extend a method for evaluating local image gradient feature descriptors to the domain of 3D shape descriptors. We perform a comparative study of three classes of such descriptors: Normal Aligned Radial Features, Fast Point Feature Histograms and Depth Kernel Descriptors; and evaluate their performance on a real-world industrial application data set. We demonstrate that noise removal enabled by the dense map representation results in major improvements in matching across all classes of descriptors as well as having a substantial positive impact on keypoint detection reliability

Place, publisher, year, edition, pages
IEEE, 2013. 3203-3209 p.
Series
IEEE International Conference on Intelligent Robots and Systems, ISSN 2153-0858
National Category
Computer Science
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-30524DOI: 10.1109/IROS.2013.6696811ISI: 000331367403040Scopus ID: 2-s2.0-84893746421ISBN: 978-1-4673-6358-7 (print)OAI: oai:DiVA.org:oru-30524DiVA: diva2:644372
Conference
2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), November 3-7, 2013. Tokyo, Japan
Available from: 2013-08-30 Created: 2013-08-30 Last updated: 2017-10-18Bibliographically approved

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Canelhas, Daniel R.Stoyanov, TodorLilienthal, Achim J.

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