Multi-Sensor Based Land Vehicles’ Positioning in Challenging GNSS Environments
The 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.
URI for this recordhttp://hdl.handle.net/1974/27839
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