Towards the recognition of 3D free-form objects

This paper investigates a new approach for the recognition of 3D objects of arbitrary shape. The proposed solution follows the principle of model-based recognition using geometric 3D models and geometric matching. It is an alternative to the classical segmentation and primitive extraction approach and provides a perspective to escape some of its difficulties to deal with free-form shapes. The heart of this new approach is a recently published iterative closest point matching algorithm, which is applied variously to a number of initial configurations. We examine methods to obtain successful matching. Our investigations refer to a recognition system used for the pose estimation of 3D industrial objects in automatic assembly, with objects obtained from range data. The recognition algorithm works directly on the 3D coordinates of the objects surface as measured by a range finder. This makes our system independent of assumptions on the objects geometry. Test and model objects are sets of 3D points to be compared with the iterative closest point matching algorithm. Substantially, we propose a set of rules to choose promising initial configurations for the iterative closest point matching; an appropriate quality measure which permits reliable decision; a method to represent the object surface in a way that improves computing time and matching quality. Examples demonstrate the feasibility of this approach to free-form recognition