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Semi-Supervised 3D Place Categorisation by Descriptor Clustering
Örebro University, School of Science and Technology. (AASS MRO Lab)ORCID iD: 0000-0001-8658-2985
Örebro University, School of Science and Technology. (AASS MRO Lab)ORCID iD: 0000-0002-9503-0602
IS lab, Halmstad University, Halmstad, Sweden.
Örebro University, School of Science and Technology. (AASS MRO Lab)ORCID iD: 0000-0002-2953-1564
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2017 (English)In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 620-625Conference paper, Published paper (Refereed)
Abstract [en]

Place categorisation; i. e., learning to group perception data into categories based on appearance; typically uses supervised learning and either visual or 2D range data.

This paper shows place categorisation from 3D data without any training phase. We show that, by leveraging the NDT histogram descriptor to compactly encode 3D point cloud appearance, in combination with standard clustering techniques, it is possible to classify public indoor data sets with accuracy comparable to, and sometimes better than, previous supervised training methods. We also demonstrate the effectiveness of this approach to outdoor data, with an added benefit of being able to hierarchically categorise places into sub-categories based on a user-selected threshold.

This technique relieves users of providing relevant training data, and only requires them to adjust the sensitivity to the number of place categories, and provide a semantic label to each category after the process is completed.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017. p. 620-625
Series
Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems, ISSN 2153-0858, E-ISSN 2153-0866
National Category
Robotics
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-61903DOI: 10.1109/IROS.2017.8202216ISI: 000426978201006Scopus ID: 2-s2.0-85041949592ISBN: 978-1-5386-2682-5 (electronic)ISBN: 978-1-5386-2683-2 (print)OAI: oai:DiVA.org:oru-61903DiVA, id: diva2:1151027
Conference
2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017) Vancouver, Canada, September 24–28, 2017
Projects
ILIAD
Funder
EU, Horizon 2020, 732737
Note

Iliad Project: http://iliad-project.eu

Available from: 2017-10-20 Created: 2017-10-20 Last updated: 2018-04-09Bibliographically approved

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Magnusson, MartinKucner, Tomasz PiotrAndreasson, HenrikLilienthal, Achim

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CiteExportLink to record
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