A Survey of Deep Learning Technology in Visual SLAM

Simultaneous localization and mapping (SLAM) is an indispensable component in robot navigation systems. The precision of SLAM in localization and mapping directly impacts the successful execution of subsequent tasks. Conventional SLAM frameworks are built upon manually designed algorithms that depend on explicit physical models. Nevertheless, in complex and dynamic environments with significant lighting changes and severe object occlusion, these traditional methods often fail to deliver the robustness and precision required for specialized robotic tasks. With the advent of deep learning and hardware computing power, an increasing number of researchers are integrating deep learning with SLAM and employing data-driven approaches to compensate for the limitations of handcrafted algorithms, particularly in establishing accurate models. This paper delves into the various aspects of SLAM where deep learning technology has been applied in recent years, elucidating the core technologies of each method. Additionally, it identifies the current challenges that necessitate resolution and suggests potential future development trends and directions.