期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 211, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2022.118624
关键词
Data -driven methods; Robotic visual grasping; Grasping detection; Computer vision
This paper presents a comprehensive survey of data-driven robotic visual grasping detection (DRVGD) for unknown objects. It reviews both object-oriented and scene-oriented aspects, providing detailed information about associated grasping representations and datasets. The challenges of DRVGD and future directions are also pointed out.
This paper presents a comprehensive survey of data-driven robotic visual grasping detection (DRVGD) for unknown objects. We review both object-oriented and scene-oriented aspects, using the DRVGD for unknown objects as a guide. Object-oriented DRVGD aims for the physical information of unknown objects, such as shape, texture, and rigidity, which can classify objects into conventional or challenging objects. Scene-oriented DRVGD focuses on unstructured scenes, which are explored in two aspects based on the position relationships of objectto-object, grasping isolated or stacked objects in unstructured scenes. In addition, this paper provides a detailed review of associated grasping representations and datasets. Finally, the challenges of DRVGD and future directions are pointed out.
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