Journal ArticleDOI
Data-driven robotic visual grasping detection for unknown objects: A problem-oriented review
TLDR
A comprehensive survey of data-driven robotic visual grasping detection (DRVGD) for unknown objects is presented in this article , where 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.Abstract:
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 object-to-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. read more
Citations
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Rotation adaptive grasping estimation network oriented to unknown objects based on novel RGB-D fusion strategy
TL;DR: Zhang et al. as mentioned in this paper fused RGB-D with shared weights in stages based on the proposed Multi-step Weight-learning Fusion (MWF) strategy to achieve spatial and rotation adaptation.
Journal ArticleDOI
Antipodal-points-aware dual-decoding network for robotic visual grasp detection oriented to multi-object clutter scenes
TL;DR: In this article , an antipodal-points-aware dual-decoding network (APDNet) is presented for grasping detection in multi-object clutter scenes, where the shared encoding strategy based on an adaptive gated fusion module is proposed in the encoder to fuse RGB-D multimodal data.
References
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