# 3-D Point Cloud Registration Using Convolutional Neural Networks
## 1 Introduction  
Automated assembly systems capable of repeatedly performing assembly tasks with high-precision and high-speed play an indispensable role in modern manufacturing lines. Intuitively, to perform these automated assembly tasks, cameras are employed for the purpose of providing vision feedback to estimate the pose (position and orientation) of objects to the system. Based on the feedback, the system controls manipulators to perform object picking and assembly tasks in order to interact with the environment. In recent years, much of the work related to the vision-based automated assembly was published. [PERSON] et al. [1; 2] proposed the dynamic compensation to realize a fast peg-in-hole alignment in the case of a large position and attitude uncertainty based on vision information provided by high-speed cameras. In References [3; 4], 3-D scenes used to estimate the pose of objects were reconstructed from multiple images acquired by several cameras. Nonetheless, these algorithms encountered some problems when extracting features as well as reconstructing 3-D feature points from images in the case of general objects that have no distinctly geometric and color features. Thus, in order to address these problems, in Reference [2], color features were added to objects which are not real in general automated assembly systems. Besides, in Reference [3], [PERSON] et al. reconstructed 3-Dpoint clouds of the object surface by employing two Charge-Coupled Device (CCD) cameras integrated with a laser beam to sweep over objects which increased the complexity in the configuration of these systems. [PERSON] (2018) proposed a vision-based control algorithm for automatic back shell assembly related to smartphone assembly based on 3-D reconstruction by two unicular fields of view of two cameras. In Reference [PERSON] et al. (2018), [PERSON] et al. developed a three-level cognitive system to perform a complex assembly task, Cranfield benchmark, based on learning by demonstration using 3-D point clouds extracted from BumbleBee2 stereo camera for the purpose of detecting the object and estimating its pose and a Kinect sensor with the aim of tracking the object in real-time, respectively. With the aforementioned explanations, 3-D point cloud data brings reliability and capability applied for most categories of objects without demanding special features. Moreover, based on great breakthroughs in manufacturing technology, 3-D cameras or 3-D scanners become increasingly common in automation fields because of redeeming features such as the precision, speed and reliability provided by these 3-D cameras. As a result, applications such as 3-D pose estimation, 3-D object detection and segmentation and 3-D point cloud matching based 3-D spatial information were focused in recent years. In particular, regarding 3-D pose estimation, [PERSON] et al. [PERSON] et al. (2018) developed a 3-D point cloud system with the aim of estimating human pose by using a depth sensor whilst a fusion framework for the purpose of 3-D modeling, object detection and pose estimation from a given set of point clouds in the case of occlusion and clutter is proposed in [PERSON] et al. (2018). Besides, a generic 3-D detection VoxelNet network was developed in Reference [PERSON] et al. (2018) to accurately detect objects in 3-D point clouds whereas [PERSON] et al. [PERSON] et al. (2018) proposed a unified architecture, PointNet, that directly employs 3-D point clouds as the input to carry out object classification, part segmentation and scene semantic parsing. Moreover, in Reference [PERSON] et al. (2018), 3-D point clouds and 2-D images were fused to segment complicated 3-D urban scenes in the case of a large scale by using deep learning. When it comes to point cloud matching, in our previous research [PERSON] et al. (2018), we proposed to apply a learning-based algorithm to resolve 2-D point cloud matching by using a deep neural network.