Physical interaction for segmentation of unknown textured and non-textured rigid objects
Physical interaction for segmentation of unknown textured and non-textured rigid objects
复制标题
用于分割未知纹理和非纹理刚性物体的物理交互
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
T. Asfour
中科院分区：
文献类型：
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作者：
David Schiebener;A. Ude;T. Asfour
我们提出了一种方法，自主交互式对象分割的人形机器人。复杂场景中未知物体的视觉分割是物体学习或抓取的重要前提，但仅通过被动观察很难实现。我们的方法使用类人机器人的操纵能力，以诱导运动的对象，从而集成了机器人的操纵和传感能力，以分割以前未知的对象。我们表明，这是可能的，没有任何人类的指导或预编程的知识，并由此产生的运动允许在一个未知的和混乱的环境中的新对象的可靠和完整的分割。我们扩展我们以前的工作，这是限制到纹理对象，通过设计新的方法来生成对象的假设和估计他们的运动后，被推的机器人。这些方法主要是基于从立体视觉获得的彩色注释的3D点的运动分析，并允许纹理以及非纹理刚性对象的分割。为了评估所获得的分割的质量，它们被用来训练一个简单的对象识别器。该方法已实施和测试的人形机器人ARMAR-III，实验结果证实了其适用于各种各样的对象，即使在高度混乱的场景。
We present an approach for autonomous interactive object segmentation by a humanoid robot. The visual segmentation of unknown objects in a complex scene is an important prerequisite for e.g. object learning or grasping, but extremely difficult to achieve through passive observation only. Our approach uses the manipulative capabilities of humanoid robots to induce motion on the object and thus integrates the robots manipulation and sensing capabilities to segment previously unknown objects. We show that this is possible without any human guidance or pre-programmed knowledge, and that the resulting motion allows for reliable and complete segmentation of new objects in an unknown and cluttered environment. We extend our previous work, which was restricted to textured objects, by devising new methods for the generation of object hypotheses and the estimation of their motion after being pushed by the robot. These methods are mainly based on the analysis of motion of color annotated 3D points obtained from stereo vision, and allow the segmentation of textured as well as non-textured rigid objects. In order to evaluate the quality of the obtained segmentations, they are used to train a simple object recognizer. The approach has been implemented and tested on the humanoid robot ARMAR-III, and the experimental results confirm its applicability on a wide variety of objects even in highly cluttered scenes.