Pose prediction of textureless objects for robot bin picking with deep learning approach

Six dimensional pose prediction of objects for robotic manipulation has received much attention in industrial applications. Despite considerable research on improving the performance of pose estimation, the problem remains challenging for industrial objects because of their textureless and mostly homogeneous properties in the presence of clutter and occlusion. This article proposes deep learning-based pose estimation by jointly detecting, segmenting and pose predicting. The binary classification branch as an attention module is suggested to improve the accuracy of instance segmentation. Using the instance information, the initial pose estimation network is designed by fusing the depth information into the grayscale image to strengthen the geometric features. Then, to obtain a highly accurate pose, an iterative network is constructed with point clouds as inputs to refine the initial pose. The networks are implemented to predict the pose of textureless object on the synthetic and real scenes. Experimental results indicate the pose estimation method is efficient and robust to pose prediction of textureless objects in cluttered and occluded scenes.