Multi-Rate Unscented Kalman Filtering for Pose Estimation

Using a car-like vehicle-platform

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Pose estimation through fusion of GNSS with secondary sensors has long been an established field. With the developments surrounding autonomous navigation over the past decade this topic has gained extra importance. In the current literature GNSS based pose and localisation is often improved through fusion with either a IMU}or VS with the goal of improving on stand-alone GNSS localisation results as well as dealing with GNSS outages. In this thesis however, all three of these sensors will be fused together using a cascade of a IMU orientation filter and a Multi-Rate UKF. This filter structure is evaluated using simulations and real-world data obtained using a created vehicle-platform. The simulated results indicate that using a Multi-Rate Unscented Kalman Filter for pose estimation is promising as the filter, when configured properly, outperforms stand-alone GNSS receivers for pose estimation. However, the real-world experiments show that the used sensors lack accuracy and precision to obtain satisfactory results.