Abstract
Automatic picking of parts is an important challenge to solve within factory automation, because it can remove tedious manual work and save labor costs. One such application involves parts that arrive with random position and orientation on a conveyor belt. The parts should be picked off the conveyor belt and placed systematically into bins. We describe a system that consists of a structured light instrument for capturing 3D data and robust methods for aligning an input 3D template with a 3D image of the scene. The method uses general and robust pre-processing steps based on geometric primitives that allow the well-known Iterative Closest Point algorithm to converge quickly and robustly to the correct solution. The method has been demonstrated for localization of car parts with random position and orientation. We believe that the method is applicable for a wide range of industrial automation problems where precise localization of 3D objects in a scene is needed.
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