Covariance Matrix Transformation Method for Absolute/Relative Measurements Fusion of Vision/IMU/GNSS Integration in Parafoil Landing

The traditional parafoil system localization methods rely highly on global navigation satellite system (GNSS). However, GNSS cannot provide the control system real-time, high-bandwidth parafoil pose. In addition, GNSS signals could be attenuated or rejected in complex environments such as mountains or canyons. In order to improve the localization precision of the parafoil system considering GNSS-challenged environments, a multistate constraint Kalman filter (MSCKF)-based vision/inertial measurement unit (IMU)/GNSS integration algorithm is explored. In contrast to traditional integration methods defining motion state in a single Cartesian coordinate, our approach jointly defines and constructs the motion state based on multiple coordinates, offering the control system the required high-bandwidth pose input directly. Furthermore, the covariance matrix transformation method is introduced to realize the smooth transition of error state between different coordinates, providing a novel solution for absolute and relative measurement fusion problems. Airsim-based simulation and real-world experiments are carried out to validate the performance of the designed integration algorithm. Experiment results indicate that the proposed algorithm can effectively integrate visual and GNSS measurements, addressing the continuous accurate and effective localization requirement for precise control of autonomous guided parafoil systems in GNSS-challenged environments.