Determining the identity and pose of occluded objects from noisy data is a critical step in interacting intelligently with an unstructured environment. Previous work has shown that local measurements of position and surface orientation may be used in a constrained search process to solve this problem, for the case of rigid objects, either two-dimensional or three-dimensional. This paper considers the more general problem of recognizing and locating objects that can vary in parameterized ways. We consider objects with rotational, translational, or scaling degrees of freedom, and objects that undergo stretching transformations. We show that the constrained search method can be extended to handle the recognition and localization of such generalized classes of object families.

Determining the identity and pose of occluded objects from noisy data is a critical part of a system's intelligent interaction with an unstructured environment. Previous work has shown that local measurements of the position and surface orientation of small patches of an object's surface may be used in a constrained search process to solve this problem for the case of rigid polygonal objects using two-dimensional sensory data, or rigid polyhedral objects using three-dimensional data. This note extends the recognition system to deal with the problem of recognizing and locating curved objects. The extension is done in two dimensions, and applies to the recognition of two-dimensional objects from two-dimensional data, or to the recognition of three-dimensional objects in stable positions from two- dimensional data.