Aiming at the problem that the loosely coupled Kalman Filter algorithm cannot perform measurement updating when the feature points are few, a head attitude tracking algorithm based on adaptive loosely-tightly coupled Extended Kalman Filtering is proposed. Firstly, according to the angular velocity measurement data from an IMU mounted on the head, the algorithm realizes the time updating of the head attitude. Then the algorithm completes the adaptive loosely-tightly coupled measurement updating according to the number of available feature points. When there are more than 4 feature points, the PnP pose is solved firstly. Then the loosely coupled measurement updating is performed by using the pose measurement. Otherwise, the tightly coupled measurement updating is performed directly by using the image measurement data. Finally, the experimental results show that the proposed algorithm can significantly expand the updating range of the head pose measurement, and improve the accuracy and stability of the head attitude tracking.
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