Fully convolutional network-based registration for augmented assembly systems

Abstract

Image-based registration methods have been widely used in augmented assembly systems. However, many challenges remain to be solved, such as low robustness and poor timeliness during registration. This paper presents a deep learning approach for registration. To reduce the workload of data collection, an automatic picture generation method is offered for deep learning algorithm, and a dataset is built for detecting the keypoints of assembly objects. We propose a fully convolutional network (FCN) model for detecting keypoints from a single RGB image. The FCN model, which consists of 13 layers, can accurately detect the location of keypoints with a low computational resource consumption. The detected keypoints are used to solve the camera position and orientation for augmented assembly registration. The experiments demonstrate that our method is robust against different rotation angles of the assembly object and against background interference. The detection accuracy is high under different camera motion blurs; to be specific, the pixel error in different directions of 640 × 480 images was only 0.9 pixels. The FCN-based registration approach was shown to be fast during augmented assembly experiments, achieving up to 30 FPS.