Visual sensors provide exclusively uncertain and partial knowledge of a scene. In this article, we present a suitable scene knowl- edge representation that makes integration and fusion of new, uncertain and partial sensor measures possible. It is based on a mixture of stochas- tic and set membership models. We consider that, for a large class of ap- plications, an approximated representation is sufficient to build a prelim- inary map of the scene. Our approximation mainly results in ellipsoidal calculus by means of a normal assumption for stochastic laws and ellip- soidal over or inner bounding for uniform laws. These approximations allow us to build an efficient estimation process integrating visual data on line. Based on this estimation scheme, optimal exploratory motions of the camera can be automatically determined. Real time experimental results validating our approach are finally given.