Simultaneous Localisation and Mapping

Simultaneous Localization and Mapping (SLAM) aims to build a map of an unknown environment while simultaneously tracking a robot's location within that map. Current research emphasizes improving the robustness and efficiency of SLAM in dynamic environments, focusing on techniques like incorporating semantic information (e.g., recognizing planes and objects), using diverse sensor fusion (e.g., LiDAR and inertial measurements), and employing advanced algorithms such as graph-based optimization and deep learning for feature extraction and map representation. These advancements are crucial for enabling autonomous navigation in complex, real-world scenarios, with applications ranging from robotics and autonomous vehicles to brain-computer interfaces and understanding spatial cognition.