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dc.contributor.advisorSeth Teller, Nicholas Roy and Jonathan Williams.en_US
dc.contributor.authorSerrano, Nathan Een_US
dc.contributor.otherMassachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2011-12-19T18:46:45Z
dc.date.available2011-12-19T18:46:45Z
dc.date.copyright2011en_US
dc.date.issued2011en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/67752
dc.descriptionThesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (p. 69).en_US
dc.description.abstractThis document presents work done to lay the foundation for an Unmanned Aerial Vehicle (UAV) system for inspecting culverts. By expanding upon prior progress creating an autonomous indoor quadrotor, many basic hardware and software issues are solved. The main new functionality needed for the culvert inspection task was to utilize the Global Positioning System (GPS) available outdoors to make up for the relative scarcity of objects visible to the Light Detection And Ranging sensor (LIDAR). The GPS data is fused in a new state estimator, which also incorporates data from the scan matcher running on the LIDAR data, as well as the data from the quadrotor's Inertial Measurement Unit (IMU). This data is combined into a single estimate of the current state (position, orientation, velocity, angular velocity, and acceleration) of the quadrotor by an Extended Kalman Filter (EKF). This new state estimate enables autonomous outdoor navigation and operation of this micro-UAV.en_US
dc.description.statementofresponsibilityby Nathan E. Serrano.en_US
dc.format.extent69 p.en_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleAutonomous quadrotor unmanned aerial vehicle for culvert inspectionen_US
dc.typeThesisen_US
dc.description.degreeM.Eng.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.identifier.oclc766546246en_US


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