Abstract

This paper discusses recent developments in Simultaneous Localization and Mapping (SLAM) combined with the use of Artificial Intelligence (AI). SLAM makes it possible for robots to build maps of unknown environments while tracking their position, but with traditional methods robots often struggle in dynamic or unstructured settings due to unclear or moving objects. Recent advances in AI and its integration into SLAM address these issues by improving feature extraction, predictive modeling, and adaptability. Convolutional and Graph Convolutional Networks enhance robustness and scalability, while transformer architectures enable efficient trajectory planning. Obstacle detection and avoidance in real-world scenarios are further reinforced by deep reinforcement learning. AI-driven innovations also introduce multi-modal sensor fusion, semantic mapping, enhanced loop closure detection, and collaborative multi-agent frameworks. Comparative studies reveal that AI-enhanced SLAM shows a higher accuracy and robustness across varied scenarios.