Vision-based control using probabilistic geometry for objects reconstruction

We first present a suitable object knowledge representation based on a mixture of stochastic and set membership models and consider an approximation resulting in ellipsoidal calculus by means of a normal assumption for stochastic laws and ellipsoidal over or inner bounding for uniform laws. Then we, build an efficient estimation process integrating visual data online and perform online and optimal exploratory motions for the camera. The control schemes are based on the maximization of the a posteriori predicted information