Semantic SLAM

Semantic SLAM aims to create 3D maps of environments that are both geometrically accurate and semantically rich, labeling objects and surfaces within the map. Current research focuses on improving the efficiency and accuracy of these maps using techniques like 3D Gaussian splatting and neural implicit representations, often incorporating multi-sensor fusion (e.g., LiDAR and cameras) and advanced algorithms for handling dynamic objects and robustly managing uncertainty. These advancements are crucial for enabling more sophisticated robot navigation, autonomous driving, and augmented reality applications that require a detailed understanding of the surrounding environment.