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Meta-rooms: Building and Maintaining Long Term Spatial Models in a Dynamic World
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.
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: 0000-0003-1189-6634
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: 0000-0002-7796-1438
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
2014 (English)In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, (IROS 2014), IEEE conference proceedings, 2014, 1854-1861 p.Conference paper, Published paper (Refereed)
Abstract [en]

We present a novel method for re-creating the static structure of cluttered office environments -which we define as the " meta-room" -from multiple observations collected by an autonomous robot equipped with an RGB-D depth camera over extended periods of time. Our method works directly with point clusters by identifying what has changed from one observation to the next, removing the dynamic elements and at the same time adding previously occluded objects to reconstruct the underlying static structure as accurately as possible. The process of constructing the meta-rooms is iterative and it is designed to incorporate new data as it becomes available, as well as to be robust to environment changes. The latest estimate of the meta-room is used to differentiate and extract clusters of dynamic objects from observations. In addition, we present a method for re-identifying the extracted dynamic objects across observations thus mapping their spatial behaviour over extended periods of time.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2014. 1854-1861 p.
Series
IEEE International Conference on Intelligent Robots and Systems, ISSN 2153-0858
Keyword [en]
Intelligent robots, Dynamic elements, Dynamic objects, Environment change, Occluded objects, Office environments, Point clusters, Spatial models, Static structures
National Category
Robotics
Identifiers
URN: urn:nbn:se:kth:diva-156601DOI: 10.1109/IROS.2014.6942806ISI: 000349834601141Scopus ID: 2-s2.0-84911488888ISBN: 978-1-4799-6934-0 (print)OAI: oai:DiVA.org:kth-156601DiVA: diva2:767282
Conference
2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2014, Palmer House Hilton Hotel Chicago, United States, 14 September 2014 through 18 September 2014
Projects
Strands
Funder
EU, FP7, Seventh Framework Programme
Note

QC 20141205

Available from: 2014-12-01 Created: 2014-12-01 Last updated: 2015-04-07Bibliographically approved

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fulltext(5505 kB)254 downloads
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Bore, NilsFolkesson, JohnJensfelt, Patric

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