We present a suitable object knowledge representation, based on a mixture of stochastic and set membership models. We consider that, for a large class of applications, an approximated representation of objects is sufficient to build a preliminary map of the scene. Our approximation mainly results in ellipsoidal calculus by means of a normal assumption for stochastic laws and ellipsoidal over or inner bounding for uniform laws. These approximations allow us to build an efficient estimation process integrating visual data online. Based on this estimation scheme, we perform online and optimal exploratory motions for the camera.
['Grégory Flandin', 'François Chaumette']
Autonomous visual exploration of complex objects