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Autonomous acquisition of visual multi-view object representations for object recognition on a humanoid robot

机译:自主获取视觉多视图对象表示形式,以在人形机器人上进行对象识别

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The autonomous acquisition of object representations which allow recognition, localization and grasping of objects in the environment is a challenging task, which has shown to be difficult. In this paper, we present a systems for autonomous acquisition of visual object representations, which endows a humanoid robot with the ability to enrich its internal object representation and allows the realization of complex visual tasks. More precisely, we present techniques for segmentation and modeling of objects held in the five-fingered robot hand. Multiple object views are generated by rotating the held objects in the robot's field of view. The acquired object representations are evaluated in the context of visual search and object recognition tasks in cluttered environments. Experimental results show successful implementation of the complete cycle from object exploration to object recognition on a humanoid robot.
机译:能够自动识别,定位和抓取环境中的物体的物体表示的自主获取是一项艰巨的任务,事实证明这是困难的。在本文中,我们提出了一种自动获取视觉对象表示的系统,该系统使类人机器人具有丰富其内部对象表示的能力,并可以实现复杂的视觉任务。更准确地说,我们介绍了对五指机器人手中的对象进行分割和建模的技术。通过旋转机器人视野中的固定对象可以生成多个对象视图。在混乱的环境中,在视觉搜索和对象识别任务的上下文中评估获取的对象表示形式。实验结果表明,在类人机器人上成功完成了从对象探索到对象识别的整个循环。

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