Ego-Motion Estimation of a Quadrotor Based on Nonlinear Observer

In this paper, an ego-motion estimation problem of a quadrotor unmanned aerial vehicle is studied. A nonlinear observer is proposed to simultaneously estimate the depth of the point features and the translational velocity of the quadrotor using visual feedback from an on-board monocular camera. Image moments defined on a rotated image plane are chosen as image features, and a decoupled image feature dynamics is achieved to facilitate the observer design. Based on the image feature dynamics and the dynamic model of the quadrotor, an observer is proposed to estimate the ego-motion of the quadrotor. Only several point features need to be tracked in consecutive acquired images, which makes the observer computationally efficient and robust. The stability of the observer is proved by means of Lyapunov analysis. Finally, computer simulations and experimental tests, including closed loop image-based visual servoing control and comparison with extended-Kalman-filter based observer, are conducted to verify the validity of the observer.