Unlocking robotic perception: comparison of deep learning methods for simultaneous localization and mapping and visual simultaneous localization and mapping in robot

Simultaneous Localization and Mapping (SLAM) and Visual SLAM are crucial technologies in robotics, allowing autonomous systems to navigate and comprehend their environment. Deep learning (DL) has become a powerful tool in driving progress in these areas, providing solutions that improve accuracy, efficiency, and resilience. This article thoroughly analyzes different deep learning techniques designed explicitly for SLAM and Visual SLAM applications in robotic systems. This work provides a detailed overview of DL roles in SLAM and VSLAM and emphasizes the differences between these two fields. Five powerful DL methods are investigated: Convolutional Neural Networks in extracting features and understanding meaning, Recurrent Neural Network in modeling temporal relationships, Deep Reinforcement Learning in developing exploration strategies, Graph Neural Network in modeling spatial relationships, and Attention Mechanisms in selectively processing information. In this research, we will examine the advantages and disadvantages of each approach in relation to robotic applications, taking into account issues such as real-time performance, resource restrictions, and adaptability to various situations. This article seeks to guide researchers and practitioners in selecting suitable deep learning algorithms to improve the capabilities of SLAM and Visual SLAM in robotic systems by combining ideas from recent research and actual implementations. The popular types of each concerned DL will be synthesized with the discussion of pros and cons.