Real-time vision-based robotic grasping is challenging in clutter. In such scene, the target object should be perceived accurately, where it may be occluded and misrecognized by many distractors including irrelevant objects and the robotic arm. In addition, the limited field of view (FOV) of camera makes it prone for objects to get out of the camera view. We develop a novel camera fusion method of pose estimation based on switching scheme for real-time robotic grasping under hybrid eye-in-hand (EIH)/eye-to-hand (ETH) configurations. The objects are locked based on occlusion-aware object detection to apply switching function for single pose estimation or multiple vision fusion. This method improves the accuracy of pose estimation and robustness of dynamic grasping under occlusion. Experimental results on pose estimation and real-time robotic grasping in clutter verify the effectiveness of the proposed method.

实时的基于视觉的机器人抓握在混乱中具有挑战性。在这样的场景中，应该准确地感知目标物体，在这里可能会被许多无关紧要的物体（包括无关的物体和机械臂）遮挡和误认。另外，相机的有限视野（FOV）使得物体容易脱​​离相机视野。我们开发了一种基于切换方案的新型姿态融合相机融合方法，用于在手眼（EIH）/手对眼（ETH）混合配置下进行实时机器人抓取。基于遮挡感知对象检测来锁定对象，以将切换功能应用于单姿势估计或多视觉融合。该方法提高了姿势估计的准确性和遮挡下动态抓握的鲁棒性。