In the field of automation and the introduction of robotic systems in production, a common problem is to pick randomly distributed products from a conveyor belt or from bins. In this work, we address 3D image capture and processing technologies with the goal of product identification and localization. A testbed is developed that includes a conveyor belt on which products are randomly arranged, a 3D measurement device that captures the 3D image in the form of a coloured point cloud, and an industrial computer that controls the entire system and processes the point clouds. The method for processing the 3D images is to first filter the point cloud, segment it, and calculate descriptors. These are then compared with the descriptors of the search object and if they match, the existence of the search object is confirmed and its position is determined. The developed method is verified using the localization of castings on a conveyor belt as an example.
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