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dc.contributor.authorAboutaleb, Ahmeden
dc.date.accessioned2020-05-25T18:29:17Z
dc.date.available2020-05-25T18:29:17Z
dc.identifier.urihttp://hdl.handle.net/1974/27839
dc.description.abstractThe car industry has a growing demand for reliable, continuous, and accurate positioning information for various applications, including routing to a specific destination, asset tracking, and, eventually future self-driving. Global navigation satellite system (GNSS) receivers have been widely used for this purpose. However, adequate GNSS positioning accuracy cannot be guaranteed in all environments due to possible satellite signal blockage, poor satellite geometry, and multipath in urban environments and downtown cores. The technological advances and low cost of micro-electro-mechanical system (MEMS) – based inertial sensors (accelerometers and gyroscopes) enabled their use inside land vehicles for various reasons, including the integration with GNSS receivers to provide positioning information that can bridge GNSS outages in challenging GNSS environments. An optimal estimation technique, such as the Kalman filter, is used to integrate the positioning solution from both the GNSS receiver and the inertial sensors. However, in dense urban areas and downtown cores where GNSS receivers may incur prolonged outages, the integrated positioning solution may become prone to rapid drift resulting in substantial position errors. Therefore, it is becoming necessary to include other sensors and systems that can be available in future land vehicles to integrate with both the GNSS receivers and inertial sensors to enhance the positioning performance in such challenging environments. The aim of this research is to design and examine the performance of a multi-sensor integrated positioning system that fuses the GNSS receiver data with not only inertial sensors but also with the three-dimensional point cloud of onboard light detection and ranging (LiDAR) system. In this thesis, a comprehensive LiDAR processing and odometry method is developed to provide a continuous and accurate positioning solution, even in challenging GNSS environments. A multi-sensor fusion based on extended Kalman filtering is also developed to integrate the LiDAR positioning information with both GNSS and inertial sensors and utilize the LiDAR updates to limit the drift in the positioning solution, even in challenging or completely denied GNSS environment. The performance of the proposed multi-sensor positioning solution is examined using several road test trajectories in both Kingston and Toronto downtown areas involving different vehicle dynamics and driving scenarios. This thesis discusses the merits and limitations of the proposed method and gives recommendations for future research.en
dc.language.isoengen
dc.relation.ispartofseriesCanadian thesesen
dc.rightsQueen's University's Thesis/Dissertation Non-Exclusive License for Deposit to QSpace and Library and Archives Canadaen
dc.rightsProQuest PhD and Master's Theses International Dissemination Agreementen
dc.rightsIntellectual Property Guidelines at Queen's Universityen
dc.rightsCopying and Preserving Your Thesisen
dc.rightsThis publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.en
dc.rightsCC0 1.0 Universal*
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.subjectIntegrated Positioningen
dc.subjectMulti-Sensor Fusionen
dc.subjectLiDAR Odometryen
dc.titleMulti-Sensor Based Land Vehicles’ Positioning in Challenging GNSS Environmentsen
dc.typethesisen
dc.description.degreeM.A.Sc.en
dc.contributor.supervisorNoureldin, Aboelmagd
dc.contributor.departmentElectrical and Computer Engineeringen
dc.degree.grantorQueen's University at Kingstonen


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Queen's University's Thesis/Dissertation Non-Exclusive License for Deposit to QSpace and Library and Archives Canada
Except where otherwise noted, this item's license is described as Queen's University's Thesis/Dissertation Non-Exclusive License for Deposit to QSpace and Library and Archives Canada