Simultaneous localization and mapping for unmanned aerial vehicles

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buir.advisorBarshan, Billur
dc.contributor.authorKök, Mehmet
dc.date.accessioned2016-01-08T18:07:12Z
dc.date.available2016-01-08T18:07:12Z
dc.date.issued2008
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.descriptionAnkara : The Department of Electrical and Electronics Engineering and the Institute of Engineering and Sciences of Bilkent University, 2008.en_US
dc.descriptionThesis (Master's) -- Bilkent University, 2008.en_US
dc.descriptionIncludes bibliographical references leaves 80-88.en_US
dc.description.abstractMost mobile robot applications require the robot to be able to localize itself in an unknown environment without prior information so that the robot can navigate and accomplish tasks. The robot must be able to build a map of the unknown environment while simultaneously localizing itself in this environment. The Simultaneous Localization and Mapping (SLAM) is the formulation of this problem which has drawn a considerable amount of interest in robotics research for the past two decades. This work focuses on the SLAM problem for single and multiple agents equipped with vision sensors. We develop a vision-based 2-D SLAM algorithm for single and multiple Unmanned Aerial Vehicles (UAV) flying at constant altitude. Using the features of images obtained from an on-board camera to identify different landmarks, we apply different approaches based on the Extended Kalman Filter (EKF), the Information Filter (IF) and the Particle Filter (PF) to the SLAM problem. We present some simulation results and provide a comparison between the different implementations. We find Particle Filter implementations to perform better in estimations when compared to EKF and IF, however EKF and IF present more consistent results.en_US
dc.description.degreeM.S.en_US
dc.description.statementofresponsibilityKök, Mehmeten_US
dc.format.extentxvi, 88 leaves, illustrations, graphsen_US
dc.identifier.urihttp://hdl.handle.net/11693/14751
dc.language.isoEnglishen_US
dc.publisherBilkent Universityen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectUAVen_US
dc.subjectmulti-agent systemsen_US
dc.subjectSIFTen_US
dc.subjectFastSLAMen_US
dc.subjectParticle Filteren_US
dc.subjectInformation Filteren_US
dc.subjectExtended Kalman Filteren_US
dc.subjectSLAMen_US
dc.subject.lccUG1242.D7 K65 2008en_US
dc.subject.lcshDrone aircraft--Control systems.en_US
dc.subject.lcshDrone aircraft--Automatic control.en_US
dc.subject.lcshSLAm (Computer program language)en_US
dc.subject.lcshDigital computer simulation.en_US
dc.subject.lcshRobots--Control systems.en_US
dc.subject.lcshCarthography.en_US
dc.titleSimultaneous localization and mapping for unmanned aerial vehiclesen_US
dc.typeThesisen_US

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Dept. of Electrical and Electronics Engineering - Master's degree