A comprehensive overview of dynamic visual SLAM and deep learning: concepts, methods and challenges
Abstract
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.