Real-time Grasp Affordance Detection of Unknown Object for Robot-Human Interaction
Real-time Grasp Affordance Detection of Unknown Object for Robot-Human Interaction
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DOI：
10.1109/smc.2019.8914202
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发表时间：
2019-10
期刊：
影响因子：
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通讯作者：
Azhar Aulia Saputra;W. Chin;N. Kubota
中科院分区：
文献类型：
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作者：
Azhar Aulia Saputra;W. Chin;N. Kubota
通过使用视觉和深度图传感器的组合，本文旨在检测实时的可承受性的抓取姿势的机器人-人交互中的移交对象的行为。示能表示检测将提供一组七维抓握信息（3D位置、3D旋转和抓握尺寸）。此外，新颖之处在于目标必须考虑接收器的抓握行为。因此，所提出的方法的结果讨论了抓取位置的机器人和接收器抓取位置的估计。从技术上讲，使用计算机视觉算法计算所需的对象检测。然后，从深度信息，拓扑图将产生使用所提出的动态密度增长神经气体。密度拓扑结构将聚焦于期望的对象。利用拓扑地图信息和机器人夹持器实施例，基于内点-外点方法计算可能的夹持位置。还考虑每个检测到的夹持中的排名信息，以从最佳和最差夹持位置进行分类。实验结果表明，所提出的工作能够检测的启示与抓握建议，在实时与低计算成本。
By using a combination of vision and depth map sensors, this paper aims at detecting the real-time affordability of gripping pose for hand-over object behavior in robot-human interaction. The affordance detection will serve a set of seven-dimension gripping information (3D location, 3D Rotation, and gripping size). Moreover, the novelty is that the goal has to consider gripping behavior of the receiver. Therefore, the result of the proposed method discusses the gripping location of the robot and the estimation of the receivers gripping location. Technically, desired objects detection is computed using a computer vision algorithm. After that, from the depth information, the topological map will be generated using the proposed dynamic density growing neural gas. The density topological structure will be focusing on the desired object. With the topological map information and robot gripper embodiment, the possible gripping position is computed based on the inlier-outlier method. Ranking information in every detected gripping is also considered for classifying from the best and the worst gripping position. Experimental results showed that the proposed work capable of detecting the affordance with a gripping recommendation in real-time with a low computational cost.