Recognition of Parametrised Models from 3D Data

This paper describes work done as part of the Oxford AGV (Autonomous Guided Vehicle) project [2] towards recognition of classes of objects to be encountered in a factory environment. \Ye address the problem of recognising an object from range-data observations as an instance of a parametric model class, and determining the values of the class parameters for that instance of the model, and the pose of the object. We represent an object class as a map from an underlying shape, a set of parameters, and some constraints on these parameters, to an instance of the class. A search of the interpretation tree is combined with a constraint network to determine the legal interpretations and parameter values using observations on a instance of the class. \Ire demonstrate the feasibility of this approach using polyhedral models and simple range-image features (position, surface normal observations).