The visual SLAM (vSLAM) is a research topic that has been developing rapidly in recent years, especially with the renewed interest in machine learning and, more particularly, deep-learning-based approaches. Nowadays, main research is carried out to improve accuracy and robustness in complex and dynamic environments. This scorching topic has reached a significant level of maturity. This paper presents a relatively detailed and easily understood survey of vSLAM within deep learning. This study attempts to meet this challenge by better organizing the literature, explaining the basic concepts and tools, and presenting the current trends. The contributions of this study can be summarized in three essential steps. The first one is to provide the state-of-the-art in an incremental way following the classical processes of vSLAM-based systems. The second is to give our short- and medium-term view of the development of this very active and evolving field. Finally, we share our opinions on this subject and its interactions with new trends and, more particularly, the deep learning paradigm. We believe that this contribution will be an overview and, more importantly, a critical and detailed vision that serves as a roadmap in the field of vSLAMs both in terms of models and concepts and in terms of associated technologies.

视觉 SLAM (vSLAM) 是近年来发展迅速的一个研究课题，尤其是随着人们对机器学习，尤其是基于深度学习的方法重新产生兴趣。如今，主要研究是在复杂和动态的环境中提高准确性和鲁棒性。这个炙手可热的话题已经达到了相当成熟的水平。本文介绍了深度学习中 vSLAM 的相对详细且易于理解的调查。本研究试图通过更好地组织文献、解释基本概念和工具以及呈现当前趋势来应对这一挑战。这项研究的贡献可以概括为三个基本步骤。第一个是遵循基于 vSLAM 的系统的经典流程，以增量方式提供最先进的技术。第二是给出我们对这个非常活跃和不断发展的领域发展的短期和中期看法。最后，我们分享我们对这个主题的看法，以及它与新趋势的相互作用，尤其是深度学习范式。我们相信，这一贡献将是一个概述，更重要的是，它是一个关键而详细的愿景，可作为 vSLAM 领域在模型和概念以及相关技术方面的路线图。