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Fine-grain Indoor Localization Infrastructure for Real-time Inspectionof Large Buildings
KTH, School of Electrical Engineering (EES), Automatic Control.
2015 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

This master thesis project is about the systems integration of an indoor localizationsystem using Ultra Wideband Impulse Radio and a drone platform which uses theParrot AR.DRONE 2.0 along with the Robot Operating System (ROS). The goal wasto use o-the-shelf components to integrate an indoor localization system which can beused for energy modelling, indoor environmental panoramas and visual inspection oflarge buildings. The system architecture is explored, implemented and then subjectedto extensive testing. Experimental results of the openRTLS UWB system illustrateaccurate readings down to 2cm for Tag positions. The Parrot AR.DRONE 2.0 waschosen due to its low cost and agility; the open source drivers of ROS ensured modularsystem software, which has room for upgrades. The drone system can be used withoptional additional sensors, either attached internally or externally, to scan indoorareas and form environmental panoramas which can quantify ambient environmentalparameters such as temperature, humidity or pressure in harsh or hard to reach areas.The live video feed from the camera facilitates navigation in NLOS conditions. Thereal time ight path of the Parrot AR.DRONE 2.0 combined with the real time positiondata of the openRTLS UWB-IR system oers a unique insight into ne-grain data oflarge buildings.

Place, publisher, year, edition, pages
2015. , 41 p.
EES Examensarbete / Master Thesis, TRITA EE 2015:88
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
URN: urn:nbn:se:kth:diva-176870OAI: diva2:868448
Available from: 2015-11-10 Created: 2015-11-10 Last updated: 2015-11-10Bibliographically approved

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