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Fast and flexible 3D object recognition solutions for machine vision applications

: Effenberger, Ira; Kühnle, Jens; Verl, Alexander


Bingham, P.R. ; Society of Photo-Optical Instrumentation Engineers -SPIE-, Bellingham/Wash.; Society for Imaging Science and Technology -IS&T-:
Image processing: Machine vision applications VI : 5 - 6 February 2013, Burlingame, California, United States
Bellingham, WA: SPIE, 2013 (Proceedings of SPIE 8661)
ISBN: 978-0-8194-9434-4
Paper 86610N
Conference "Image Processing - Machine Vision Applications" <6, 2013, Burlingame/Calif.>
Conference Paper
Fraunhofer IPA ()
3D-Objekterkennung; Punktwolke; point cloud; object segmentation; Segmentierung (Bildverarbeitung); object localisation; Best-Fit; Griff in die Kiste; bin-picking; machine vision; Objekterkennung; Bildverarbeitung

In automation and handling engineering, supplying work pieces between different stages along the production process chain is of special interest. Often the parts are stored unordered in bins or lattice boxes and hence have to be separated and ordered for feeding purposes. An alternative to complex and spacious mechanical systems such as bowl feeders or conveyor belts, which are typically adapted to the parts' geometry, is using a robot to grip the work pieces out of a bin or from a belt. Such applications are in need of reliable and precise computer-aided object detection and localization systems. For a restricted range of parts, there exists a variety of 2D image processing algorithms that solve the recognition problem. However, these methods are often not well suited for the localization of randomly stored parts. In this paper we present a fast and flexible 3D object recognizer that localizes objects by identifying primitive features within the objects. Since technica l work pieces typically consist to a substantial degree of geometric primitives such as planes, cylinders and cones, such features usually carry enough information in order to determine the position of the entire object. Our algorithms use 3D best-fitting combined with an intelligent data pre-processing step. The capability and performance of this approach is shown by applying the algorithms to real data sets of different industrial test parts in a prototypical bin picking demonstration system.