Automatic inspection of mechanical parts using geometric models and laser range finder data

Abstract

This paper addresses the problems associated in processing arrays of depth data in order to achieve the goal of automatic inspection of mechanical parts, i. e. developing general model-based inspection strategies that can be applied to a range of objects. The main problems in processing this data are segmenting out reliable primitives from the data and matching these primitives to those in a stored geometric model of an object. The ability of a 3D vision system to provide depth data accurate enough to perform automatic inspection tasks was until recently only possible at a short range from an object, typically a few centimetres. However it is now possible to produce dense data from a vision system situated further from the object, typically half a metre to a metre. Such a system is outlined. Some current model-based matching techniques are assessed for their suitability for employment in inspection type tasks. One approach is adopted and modifications that improve the efficiency and accuracy of the method for inspection purposes are presented. Finally, an inspection strategy is outlined and its performance assessed. Results are presented on both artificial and real depth data.