During industrial overhauling processes, several small parts and fasteners must be sorted and packed into different containers for reuse. Most industrial bin picking solutions use either a CAD model of the objects for comparison with the obtained 3D point clouds or complementary approaches, such as stereo cameras and laser sensors. However, obtaining CAD models may be infeasible for all types of small parts. In addition, industrial small parts have characteristics (e.g., light reflections in ambient light) that make the picking task even more challenging even when using laser and stereo cameras. In this paper, we propose an approach that solves these problems by automatically segmenting small parts and classifying their orientation and obtaining a grasp point using 2D images. The proposed approach obtained segmentation accuracy of 80% by applying a Mask R-CNN model trained on 10 annotated images. Moreover, it computes the orientation and grasp point of the pickable objects using Mask R-CNN or a combination of PCA and Image Moment. The proposed approach is a first step towards an automated bin picking system in overhaul processes that reduces costs and time by segmenting pickable small parts to be picked by a robot.
Paper
Full text
2D Orientation and Grasp Point Computation for Bin Picking in Overhaul Processes
Semantic Scholar · Engineering · 2020
Abstract
During industrial overhauling processes, several small parts and fasteners must be sorted and packed into different containers for reuse. Most industrial bin picking solutions use either a CAD model of the objects for comparison with the obtained 3D point clouds or complementary approaches, such as stereo cameras and laser sensors. However, obtaining CAD models may be infeasible for all types of small parts. In addition, industrial small parts have characteristics (e.g., light reflections in ambient light) that make the picking task even more challenging even when using laser and stereo cameras. In this paper, we propose an approach that solves these problems by automatically segmenting small parts and classifying their orientation and obtaining a grasp point using 2D images. The proposed approach obtained segmentation accuracy of 80% by applying a Mask R-CNN model trained on 10 annotated images. Moreover, it computes the orientation and grasp point of the pickable objects using Mask R-CNN or a combination of PCA and Image Moment. The proposed approach is a first step towards an automated bin picking system in overhaul processes that reduces costs and time by segmenting pickable small parts to be picked by a robot.