Implementation of Real-Time Simultaneous Localization and Mapping with Particle Filter

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dc.contributor.advisorDavis, Christopher Cen_US
dc.contributor.authorPatel, Jaymiten_US
dc.contributor.departmentElectrical Engineeringen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2013-07-04T05:30:17Z
dc.date.available2013-07-04T05:30:17Z
dc.date.issued2012en_US
dc.description.abstractThe goal of this thesis is to use Particle Filters to Simultaneously Localize a mobile robot in an unknown environment and produce an accurate Map. The theory behind Monte Carlo Localization and Occupancy Grid Maps is introduced and compared with improvements to the Particle Filter such as the Shared Gridmaps and Variance Sampler. A Particle Filter algorithm is developed to use sonar measurements to create occupancy maps, and inertial sensors and wheel encoders to update robot's odometry. The Algorithm is applied to a four-wheel robot in an indoor environment with hallways and is successful in creating detailed maps of the test location and accurate estimate of the robot's state.en_US
dc.identifier.urihttp://hdl.handle.net/1903/14247
dc.subject.pqcontrolledRoboticsen_US
dc.subject.pqcontrolledElectrical engineeringen_US
dc.subject.pqcontrolledComputer engineeringen_US
dc.subject.pquncontrolledIndoor Environmenten_US
dc.subject.pquncontrolledMobile Roboten_US
dc.subject.pquncontrolledParticle Filteren_US
dc.subject.pquncontrolledSLAMen_US
dc.subject.pquncontrolledUltrasonic Sensorsen_US
dc.titleImplementation of Real-Time Simultaneous Localization and Mapping with Particle Filteren_US
dc.typeThesisen_US

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