Monte Carlo localization for mobile robots in dynamic environments

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dc.creatorBansail, Ajay
dc.date.accessioned2016-11-14T23:12:50Z
dc.date.available2011-02-18T19:20:30Z
dc.date.available2016-11-14T23:12:50Z
dc.date.issued2002-05
dc.degree.departmentComputer Scienceen_US
dc.description.abstractMobile robot localization is the problem of determining a robot's pose from sensor data. This thesis presents a family of probabilistic localization algorithms known as Monte Carlo Localization (MCL). MCL algorithms represent a robot's belief by a set of weighted hypotheses (samples) which approximate the posterior under a common Bayesian formulation of the localization problem. The MCL algorithm does not work well in dynamic environments. Thus, building on the basic MCL algorithm, this thesis develops a dynamic version of the algorithm, which applies filtering techniques to filter out the unexpected data and work well in dynamic environments. Systematic empirical results illustrate the robustness and computational efficiency of the approach.
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/2346/10399en_US
dc.language.isoeng
dc.publisherTexas Tech Universityen_US
dc.rights.availabilityUnrestricted.
dc.subjectMonte Carlo methoden_US
dc.subjectMobile robotsen_US
dc.subjectRobots -- Control systemsen_US
dc.titleMonte Carlo localization for mobile robots in dynamic environments
dc.typeThesis

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