Grasping partially known objects in unstructured environments is one of the most challenging issues in robotics. In this paper, we present a real-time and robust approach for detecting and grasping different objects with a robot manipulator in a partially unstructured scenario. The proposed method is based on two steps: 1) the generation of a background model to localize the objects of interest and 2) the use of depth information to find the grasp pose. Quantitative experiments using a 7 degrees-of-freedom manipulator on different objects demonstrates the effectiveness of the proposed approach.
Real-time Object Detection and Grasping Using Background Subtraction in an Industrial Scenario
M. Sileo;D. D. Bloisi;F. Pierri
2021-01-01
Abstract
Grasping partially known objects in unstructured environments is one of the most challenging issues in robotics. In this paper, we present a real-time and robust approach for detecting and grasping different objects with a robot manipulator in a partially unstructured scenario. The proposed method is based on two steps: 1) the generation of a background model to localize the objects of interest and 2) the use of depth information to find the grasp pose. Quantitative experiments using a 7 degrees-of-freedom manipulator on different objects demonstrates the effectiveness of the proposed approach.File in questo prodotto:
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