Multirobot Localization Using Heuristically Tuned Extended Kalman Filter

FieldValue
dc.contributor.authorMasinjila, Ruslan
dc.date.accessioned2016-11-23T14:09:06Z
dc.date.available2016-11-23T14:09:06Z
dc.date.issued2016
dc.identifier.urihttp://hdl.handle.net/10393/35489
dc.identifier.urihttp://dx.doi.org/10.20381/ruor-447
dc.description.abstractA mobile robot needs to know its pose (position and orientation) in order to navigate and perform useful tasks. The problem of determining this pose with respect to a global or local frame is called localisation, and is a key component in providing autonomy to mobile robots. Thus, localisation answers the question Where am I? from the robot’s perspective. Localisation involving a single robot is a widely explored and documented problem in mobile robotics. The basic idea behind most documented localisation techniques involves the optimum combination of noisy and uncertain information that comes from various robot’s sensors. However, many complex robotic applications require multiple robots to work together and share information among themselves in order to successfully and efficiently accomplish certain tasks. This leads to research in collaborative localisation involving multiple robots. Several studies have shown that when multiple robots collaboratively localise themselves, the resulting accuracy in their estimated positions and orientations outperforms that of a single robot, especially in scenarios where robots do not have access to information about their surrounding environment. This thesis presents the main theme of most of the existing collaborative, multi-robot localisation solutions, and proposes an alternative or complementary solution to some of the existing challenges in multirobot localisation. Specifically, in this thesis, a heuristically tuned Extended Kalman Filter is proposed to localise a group of mobile robots. Simulations show that when certain conditions are met, the proposed tuning method significantly improves the accuracy and reliability of poses estimated by the Extended Kalman Filter. Real world experiments performed on custom-made robotic platforms validate the simulation results.
dc.language.isoen
dc.publisherUniversité d'Ottawa / University of Ottawa
dc.subjectExtended Kalman Filter
dc.subjectSensor fusion
dc.subjectHeuristics
dc.subjectconsistency
dc.subjectRobot Operating System
dc.titleMultirobot Localization Using Heuristically Tuned Extended Kalman Filter
dc.typeThesis
dc.contributor.supervisorPayeur, Pierre
thesis.degree.nameMASc
thesis.degree.levelMasters
thesis.degree.disciplineGénie / Engineering
uottawa.departmentScience informatique et génie électrique / Electrical Engineering and Computer Science
CollectionThèses, 2011 - // Theses, 2011 -

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