This paper proposes a pose estimation system for robot grasping based on a novel Object Affordance Detection and Segmentation (OADS) network. The proposed system consists of four modules: (1) OADS network; (2) point cloud extraction; (3) object pose estimation; (4) grasp pose estimation. Based on the OADS network, the proposed system achieves affordance-based object pose estimation results. The proposed grasp pose estimation system is evaluated on a laboratory-made dual-arm robot. Experimental results show that the proposed system achieves high detection rate and high accuracy in affordance detection and segmentation tasks, leading to a high success rate in object grasping tasks with lab-made dual-arm robot.


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    Title :

    A Real-time Affordance-based Object Pose Estimation Approach for Robotic Grasp Pose Estimation


    Contributors:


    Publication date :

    2023-07-27


    Size :

    933285 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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