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Semi-Supervised 3D Place Categorisation by Descriptor Clustering
MRO lab, Centre for Applied Autonomous Sensor Systems (AASS), Örebro University, Örebro, Sweden.ORCID iD: 0000-0001-8658-2985
MRO lab, Centre for Applied Autonomous Sensor Systems (AASS), Örebro University, Örebro, Sweden.ORCID iD: 0000-0002-9503-0602
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0003-3498-0783
MRO lab, Centre for Applied Autonomous Sensor Systems (AASS), Örebro University, Örebro, Sweden.ORCID iD: 0000-0002-2953-1564
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2017 (English)In: IROS Vancouver 2017: Conference Digest, Piscataway: 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. © 2017 IEEE.

Place, publisher, year, edition, pages
Piscataway: Institute of Electrical and Electronics Engineers (IEEE), 2017. p. 620-625
Keywords [en]
Classification (of information), Semantics, 3D point cloud, Clustering techniques, Descriptors, Semantic labels, Semi-supervised, Supervised trainings, Training data, Training phase, Intelligent robots
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:hh:diva-40210DOI: 10.1109/IROS.2017.8202216ISI: 000426978201006Scopus ID: 2-s2.0-85041949592Libris ID: 21822274OAI: oai:DiVA.org:hh-40210DiVA, id: diva2:1356274
Conference
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, Canada, September 24-28, 2017
Funder
EU, Horizon 2020, 732737Available from: 2019-10-01 Created: 2019-10-01 Last updated: 2019-10-02

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Gholami Shahbandi, Saeed

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