Handling Uncertainty in 3D Object Recognition Using Bayesian Networks

In this paper we show how the uncertainty within a 3d recognition process can be modeled using Bayesian nets. Reliable object features in terms of object rims are introduced to allow a robust recognition of industrial free-form objects. Dependencies between observed features and the objects are modeled within the Bayesian net. An algorithm to build the Bayesian net from a set of CAD models is introduced. In the recognition, entering evidence into the Bayesian net reduces the set of possible object hypotheses. Furthermore, the expected change of the joint probability distribution allows an integration of decision reasoning in the Bayesian propagation. The selection of the optimal, next action is incorporated into the Bayesian nets to reduce the uncertainty.

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