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Data-driven robotic visual grasping detection for unknown objects: A problem-oriented review
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AI
DOI：10.1016/j.eswa.2022.118624.png)
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Abstract
En 中文
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.
Keywords:
Data -driven methods
Robotic visual grasping
Grasping detection
Computer vision
Journal
IF:
7.5
Papers:
2.9W
Citations:
10.2W