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.

基于图像的配准方法已被广泛用于增强装配系统中。但是，仍有许多挑战有待解决，例如鲁棒性低和注册过程中的时效性差。本文提出了一种用于注册的深度学习方法。为了减少数据收集的工作量，提供了一种用于深度学习算法的自动图片生成方法，并建立了一个用于检测装配对象关键点的数据集。我们提出了一种完全卷积网络（FCN）模型，用于从单个RGB图像中检测关键点。FCN模型由13层组成，可以以较低的计算资源消耗来准确检测关键点的位置。检测到的关键点用于解决相机的位置和方向，以增强装配体的配准。实验表明，我们的方法对于组装对象的不同旋转角度和背景干扰具有鲁棒性。在不同的摄像机运动模糊下，检测精度很高；具体而言，640×480图像在不同方向上的像素误差仅为0.9像素。在增强装配实验中，基于FCN的注册方法被证明是快速的，可达到30 FPS。