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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    Titel :

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


    Beteiligte:
    Wong, Shang-Wen (Autor:in) / Chiu, Yu-Chen (Autor:in) / Tsai, Chi-Yi (Autor:in)


    Erscheinungsdatum :

    27.07.2023


    Format / Umfang :

    933285 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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