Point cloud registration aims to find a rigid transformation for aligning one point cloud to another. Such registration is a fundamental problem in computer vision and robotics, and has been widely used in various applications, including 3D reconstruction, simultaneous localization and mapping, and autonomous driving. Over the last decades, numerous researchers have devoted themselves to tackling this challenging problem. The success of deep learning in high-level vision tasks has recently been extended to different geometric vision tasks. Various types of deep learning based point cloud registration methods have been proposed to exploit different aspects of the problem. However, a comprehensive overview of these approaches remains missing. To this end, in this paper, we summarize the recent progress in this area and present a comprehensive overview regarding deep learning based point cloud registration. We classify the popular approaches into different categories such as correspondences-based and correspondences-free approaches, with effective modules, i.e., feature extractor, matching, outlier rejection, and motion estimation modules. Furthermore, we discuss the merits and demerits of such approaches in detail. Finally, we provide a systematic and compact framework for currently proposed methods and discuss directions of future research.