GNSS/IMU seonsor fusion integration framework

Kurzfassung

Global Navigation Satellite Systems (GNSS) have become increasingly widespread, with more satellites being launched and better signal availability than ever before. Due to their convenience and accessibility, GNSS technologies are now being used in a wide range of applications, among them, autonomous navigation for vehicles such as cars, ships, and drones. High accuracy and real-time positioning is crucial for these applications which are rapidly growing. Despite advancements in satellite technologies, GNSS alone still faces inherent limitations: the update rate is relatively low, and signal blockage or multipath effects in urban environments can hinder continuous and reliable positioning. In contrast, inertial sensors are self-contained and provide high-frequency motion data, making them ideal for capturing rapid dynamics. However, they suffer from drift over time due to the accumulation of measurement errors. To overcome these limitations, this thesis presents an online oriented GNSS and Inertial Navigation System (INS) integration system that directly processes GNSS and IMU raw measureemnts from the receiver. A quaternion-based Error State Kalman Filter (ESKF) is implemented to perform sensor fusion, combining the advantages of both systems while maintaining numerical stability. The proposed framework includes a complete data extraction pipeline capable of decoding GNSS and IMU measurements directly from the receivers binary messages, enabling online processing without relying on RINEX files. Experimental validation is carried out using simulated datasets, and the performance is analyzed under various scenarios, including temporary GNSS outages, to assess the robustness of the developed system.