Sensor fusion method using GPS/IMU data for fast UAV surveillance video frame registration
Yi Wang, Richard R. Schultz, Ronald Adrey Fevig · 2009
This paper proposes an innovative framework for fast image registration of UAV surveillance video frames by fusing the data from a GPS receiver high-frequency IMU sensor (Piccolo autopilot) and a feature-domain registration method through a non-linear filter. The high-frequency imprecise data from the Piccolo autopilot is refined by the low-frequency precise data from our feature-domain based random M least squares (RMLS) method. The projective transformation model is chosen to achieve high precision. The state and measurement models are non-linear to approximate the real-world imaging dynamics. A periodic hybrid particle filter (PHPF), composed of extended Kalman filter (EKF) and unscented Kalman filter (UKF), is proposed to minimize running time while maintaining accuracy. Both the efficiency and effectiveness of the proposed algorithm will be evaluated through our experiments.