The paper proposes an algorithm for object motion estimation. It is assumed that the object motion is composed of primitive motions and the total motion is generated by simply switching these primitive motions. The proposed estimator for visual servoing has two stages: Kalman filter and validation gate. Models of primitive motions are connected and an augmented system is designed. Each primitive motion corresponds to an invariant subset in the state space of the augmented system. The motion switching is generated by a state transition among the state subsets. A Kalman filter is used to predict the object motion on the basis of the augmented system. Switching of actual object motion is detected by the validation gate. The validation gate is implemented by nonlinear least square fitting of the current state onto the nearest state subsets. Real-time experiments of object tracking demonstrate the effectiveness of the proposed estimator.

The paper proposes a visual servoing scheme based on object motion estimation. A long term prediction algorithm with multiple motion models is developed. The object position and velocity of a few hundreds of samples ahead is predicted. Thus object tracking without visual information can be achieved during this period. As an example, pick up task of moving object is conducted. The task requires object motion prediction because the hand mounted camera loses the object when the hand approaches the object. Simulations show convergence of prediction. Real-time experiments of picking up a moving object demonstrate the effectiveness of the proposed long term predictor.