In recent years, dynamic visual SLAM techniques have been widely used in autonomous navigation, augmented reality, and virtual reality. However, the increasing demand for computational resources by SLAM techniques limits its application on resource-constrained mobile devices. MEC technology combined with 5G ultra-dense networks enables complex computational tasks in visual SLAM systems to be offloaded to edge computing servers, thus breaking the resource constraints of terminals and meeting real-time computing requirements. This paper firstly introduces the research results in the field of visual SLAM in detail through three categories: static SLAM, dynamic SLAM, and SLAM techniques combined with deep learning. Secondly, the three major parts of the technology comparison between mobile edge computing and mobile cloud computing, 5G ultra-dense networking technology, and MEC and UDN integration technology are introduced to sort out the basic technologies related to the application of 5G ultra-dense network to offload complex computing tasks from visual SLAM systems to edge computing servers.

近年来，动态视觉 SLAM 技术在自主导航、增强现实和虚拟现实中得到了广泛的应用。然而，SLAM 技术对计算资源日益增长的需求限制了其在资源受限的移动设备上的应用。MEC技术结合5G超密集网络，可以将视觉SLAM系统中的复杂计算任务卸载到边缘计算服务器上，从而打破终端资源限制，满足实时计算需求。本文首先通过静态SLAM、动态SLAM、结合深度学习的SLAM技术三大类，详细介绍了视觉SLAM领域的研究成果。其次，移动边缘计算与移动云计算的三大部分技术对比，