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Instance Segmentation of Visible and Occluded Regions for Finding and Picking Target from a Pile of Objects
arXiv - CS - Robotics Pub Date : 2020-01-21 , DOI: arxiv-2001.07475
Kentaro Wada, Shingo Kitagawa, Kei Okada, Masayuki Inaba

We present a robotic system for picking a target from a pile of objects that is capable of finding and grasping the target object by removing obstacles in the appropriate order. The fundamental idea is to segment instances with both visible and occluded masks, which we call `instance occlusion segmentation'. To achieve this, we extend an existing instance segmentation model with a novel `relook' architecture, in which the model explicitly learns the inter-instance relationship. Also, by using image synthesis, we make the system capable of handling new objects without human annotations. The experimental results show the effectiveness of the relook architecture when compared with a conventional model and of the image synthesis when compared to a human-annotated dataset. We also demonstrate the capability of our system to achieve picking a target in a cluttered environment with a real robot.

中文翻译:

用于从一堆对象中寻找和挑选目标的可见区域和遮挡区域的实例分割

我们提出了一种用于从一堆物体中挑选目标的机器人系统,该系统能够通过以适当的顺序移除障碍物来找到并抓住目标物体。基本思想是用可见和被遮挡的掩码分割实例,我们称之为“实例遮挡分割”。为了实现这一点,我们使用新颖的“relook”架构扩展了现有的实例分割模型,其中该模型明确地学习了实例间关系。此外,通过使用图像合成,我们使系统能够在没有人工注释的情况下处理新对象。实验结果表明,与传统模型相比,relook 架构的有效性以及与人工注释数据集相比的图像合成的有效性。
更新日期:2020-01-22
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