A state estimator of UAV using time-delayed position and IMU data

The existence of noise and time delay in sensing makes state observation difficult for flying robots. In this paper, we propose a state observer to estimate states of unmanned aerial vehicle (UAV) in a cluttered environment where only noised and delayed position data is available. We implement an extended Kalman filter to fuse position data and Inertial Measurement Unit (IMU) data. IMU is also used to compensate for the delay in position data. Compared with previous works, we improve the accuracy of position observation and eliminate time delay through sensor fusion. Simulation and analysis are presented to verify the performance of this method.