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Hierarchical Multi-modal Place Categorization
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.ORCID iD: 0000-0002-1396-0102
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.ORCID iD: 0000-0002-1170-7162
2011 (English)In: Proc. of the European Conference on Mobile Robotics (ECMR'11), 2011Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we present an hierarchical approach to place categorization. Low level sensory data is processed into more abstract concept, named properties of space. The framework allows for fusing information from heterogeneous sensory modalities and a range of derivatives of their data. Place categories are defined based on the properties that decouples them from the low level sensory data. This gives for better scalability, both in terms of memory and computations. The probabilistic inference is performed in a chain graph which supports incremental learning of the room category models. Experimental results are presented where the shape, size and appearance of the rooms are used as properties along with the number of objects of certain classes and the topology of space.

Place, publisher, year, edition, pages
2011.
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:kth:diva-51160OAI: oai:DiVA.org:kth-51160DiVA: diva2:463516
Conference
Proc. of the European Conference on Mobile Robotics (ECMR'11). Örebro, Sweden. Sep 7-9 2011
Note
QC 20111209Available from: 2011-12-09 Created: 2011-12-09 Last updated: 2011-12-09Bibliographically approved

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Pronobis, AndrzejJensfelt, Patric

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