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On the use of Unmanned Aerial Vehicles for Autonomous Object Modeling
KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.ORCID iD: 0000-0002-3111-3812
KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
2017 (English)Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we present an end to end objectmodeling pipeline for an unmanned aerial vehicle (UAV). Wecontribute a UAV system which is able to autonomously plan apath, navigate, acquire views of an object in the environmentfrom which a model is built. The UAV does collision checkingof the path and navigates only to those areas deemed safe. Thedata acquired is sent to a registration system which segmentsout the object of interest and fuses the data. We also show aqualitative comparison of our results with previous work.

Place, publisher, year, edition, pages
2017.
Keyword [en]
Unmanned Aerial Vehicle (UAV), Object modeling, RGB-D
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-216994OAI: oai:DiVA.org:kth-216994DiVA: diva2:1152924
Conference
2017 European Conference on Mobile Robots (ECMR), 6 Sep, 2017 - 8 Sep, 2017. Paris, France
Note

QC 20171114

Available from: 2017-10-26 Created: 2017-10-26 Last updated: 2017-11-14Bibliographically approved

Open Access in DiVA

UAV_for_Autonomous_Object_Modeling_ECMR_2017(12089 kB)9 downloads
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