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Automatic Room Segmentation From Unstructured 3-D Data of Indoor Environments
KTH, Skolan för datavetenskap och kommunikation (CSC), Robotik, perception och lärande, RPL. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.ORCID-id: 0000-0002-3111-3812
2017 (engelsk)Inngår i: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 2, nr 2, s. 749-756Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

We present an automatic approach for the task of reconstructing a 2-D floor plan from unstructured point clouds of building interiors. Our approach emphasizes accurate and robust detection of building structural elements and, unlike previous approaches, does not require prior knowledge of scanning device poses. The reconstruction task is formulated as a multiclass labeling problem that we approach using energy minimization. We use intuitive priors to define the costs for the energy minimization problem and rely on accurate wall and opening detection algorithms to ensure robustness. We provide detailed experimental evaluation results, both qualitative and quantitative, against state-of-the-art methods and labeled ground-truth data.

sted, utgiver, år, opplag, sider
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC , 2017. Vol. 2, nr 2, s. 749-756
Emneord [en]
Mapping, RGB-D perception, semantic scene understanding
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Identifikatorer
URN: urn:nbn:se:kth:diva-217456DOI: 10.1109/LRA.2017.2651939ISI: 000413736600049OAI: oai:DiVA.org:kth-217456DiVA, id: diva2:1158012
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QC 20171117

Tilgjengelig fra: 2017-11-17 Laget: 2017-11-17 Sist oppdatert: 2018-01-13bibliografisk kontrollert

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