CAD-Based Vision: Object Recognition in Cluttered Range Images Using Recognition Strategies

This paper addresses the problem of recognizing an object in a given scene using a three-dimensional model of the object. The scene may contain several overlapping objects, arbitrarily positioned and oriented. A laser range scanner is used to collect three-dimensional (3D) data points from the scene. The collected data is segmented into surface patches, and the segments are used to calculate various 3D surface properties. The CAD models are designed using commercially available CADKEY and accessed via the industry standard IGES. The models are analyzed off-line to derive various geometric features, their relationships, and their attributes. A strategy for identifying each model is then automatically generated and stored. The strategy is applied at run-time to complete the task of object recognition. The goal of the generated strategy is to select the model′s geometric features in the sequence which may best be used to identify and locate the model in the scene. The generated strategy is guided by several factors, such as the visibility, detectability, the frequency of occurrence, and the topology of the features. The paper concludes with examples of the generated strategies and their application to object recognition in several scenes containing multiple objects.