论文标题
使用基于图像的视觉伺服宣传来控制闭环视觉抓地的最终阶段
Control of the Final-Phase of Closed-Loop Visual Grasping using Image-Based Visual Servoing
论文作者
论文摘要
本文认为，RGB-D摄像头不再能够提供有效的深度信息，其中RGB-D摄像头的最终进近阶段。许多当前的机器人抓握控制器不是闭环，因此无法移动对象。基于RGB-D图像的闭环控制器可以跟踪移动对象，但是当传感器的最小对象距离在抓握之前违反时失败。为了克服这一点，我们建议使用基于图像的视觉伺服宣传（IBV）将机器人引导到使用摄像机RGB信息的对象相关抓姿势。 IBV稳健地将相机移至隐式定义的目标姿势，以图像平面特征配置为由。在这项工作中，从RGB-D数据中预测了目标图像特征坐标，以启用仅RGB的跟踪，一旦深度数据变得不可用 - 这使得对以前看不见的移动对象更可靠地掌握。提供了实验结果。
This paper considers the final approach phase of visual-closed-loop grasping where the RGB-D camera is no longer able to provide valid depth information. Many current robotic grasping controllers are not closed-loop and therefore fail for moving objects. Closed-loop grasp controllers based on RGB-D imagery can track a moving object, but fail when the sensor's minimum object distance is violated just before grasping. To overcome this we propose the use of image-based visual servoing (IBVS) to guide the robot to the object-relative grasp pose using camera RGB information. IBVS robustly moves the camera to a goal pose defined implicitly in terms of an image-plane feature configuration. In this work, the goal image feature coordinates are predicted from RGB-D data to enable RGB-only tracking once depth data becomes unavailable -- this enables more reliable grasping of previously unseen moving objects. Experimental results are provided.
