In this paper, we present Segmentation-Based Grasp Detection Network (SGDN) to predict a feasible robotic grasping for a unsymmetrical three-finger robotic gripper using RGB images. The feasible grasping of a target should be a collection of grasp regions with the same grasp angle and width. In other words, a simplified planar grasp representation should be pixel-level rather than region-level such as five-dimensional grasp representation.Therefore, we propose a pixel-level grasp representation, oriented base-fixed triangle. It is also more suitable for unsymmetrical three-finger gripper which cannot grasp symmetrically when grasping some objects, the grasp angle is at [0, 2{\pi}) instead of [0, {\pi}) of parallel plate gripper.In order to predict the appropriate grasp region and its corresponding grasp angle and width in the RGB image, SGDN uses DeepLabv3+ as a feature extractor, and uses a three-channel grasp predictor to predict feasible oriented base-fixed triangle grasp representation of each pixel.On the re-annotated Cornell Grasp Dataset, our model achieves an accuracy of 96.8% and 92.27% on image-wise split and object-wise split respectively, and obtains accurate predictions consistent with the state-of-the-art methods.

中文翻译：

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SGDN：基于分割的非对称三指夹持器抓取检测网络

在本文中，我们提出了基于分割的抓取检测网络 (SGDN)，以使用 RGB 图像预测不对称三指机器人抓手的可行机器人抓取。目标的可行抓取应该是抓取角度和宽度相同的抓取区域的集合。换句话说，一个简化的平面抓取表示应该是像素级的，而不是区域级的，比如五维抓取表示。因此，我们提出了一个像素级的抓取表示，有向基固定三角形。它也更适用于抓取某些物体时不能对称抓取的非对称三指抓手，抓取角度在[0, 2{\pi})而不是平行板抓手的[0, {\pi})。为了预测RGB图像中合适的抓取区域及其对应的抓取角度和宽度，