Visual motion estimation of 3D objects: an adaptive extended Kalman filter approach

An algorithm for the visual estimation of the pose of a moving object is presented in this paper. The algorithm exploits the prediction capability of the extended Kalman Filter to realize in real time a dynamic optimal selection of the object image features used for pose estimation. The robustness of the system with respect to the measurement noise and modelling errors is enhanced by using an adaptive scheme. Experimental case studies are presented to prove the effectiveness of the proposed approach.