In this article, a novel, efficient grasp synthesis method is introduced that can be used for closed-loop robotic grasping. Using only a single monocular camera, the proposed approach can detect contour information from an image in real time and then determine the precise position of an object to be grasped by matching its contour with a given template. This approach is much lighter than the currently prevailing methods, especially vision-based deep-learning techniques, in that it requires no prior training. With the use of the state-of-the-art techniques of edge detection, superpixel segmentation, and shape matching, our visual servoing method does not rely on accurate camera calibration or position control and is able to adapt to dynamic environments. Experiments show that the approach provides high levels of compliance, performance, and robustness under diverse experiment environments.

中文翻译：

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具有自然背景的机器人物体识别和抓取

在本文中，介绍了一种新颖、高效的抓取合成方法，可用于闭环机器人抓取。仅使用单个单目相机，所提出的方法可以实时检测图像中的轮廓信息，然后通过将其轮廓与给定模板进行匹配来确定要抓取的对象的精确位置。这种方法比目前流行的方法要轻得多，尤其是基于视觉的深度学习技术，因为它不需要事先训练。通过使用最先进的边缘检测、超像素分割和形状匹配技术，我们的视觉伺服方法不依赖于精确的相机校准或位置控制，能够适应动态环境。实验表明，该方法提供了高水平的合规性、性能、