The research deals with the problem of automatically building scene models from data gathered by mobile sensors. Our previous work has investigated techniques for sensor motion planning based on the feedback derived from the (bounded) uncertainty of the inferred scene models. This work has led to the development of local planning strategies aimed at reducing the uncertainty in the parameters defining models of the scene geometry. A natural extension of this work is to devise planning strategies embodying, in a principled probabilistic sense, prior uncertain knowledge about both the objects in the scenes and their shapes. Along this way, Bayesian connectionist classification methods seems promising in order to:

*Learn*a model-based soft recognition of the objects in the scene, in the form of probability distributions on the possible objects themselves;*Score*the above distributions by producing a global measure of uncertainty (ambiguity);*Inverse-map*the measure of ambiguity onto model parameters uncertainties to drive the local sensor motion planning strategies.

F. Callari, F.P. Ferrie

Connectionist Model

Mon Nov 13 10:43:02 EST 1995