LiDAR SLAM

LiDAR SLAM (Simultaneous Localization and Mapping) aims to build accurate 3D maps of an environment while simultaneously tracking a robot's position within it using LiDAR sensor data. Current research emphasizes improving the robustness and efficiency of SLAM algorithms, particularly in challenging scenarios like large-scale mapping, geometrically degenerate environments, and dynamic settings, often employing neural network architectures (e.g., implicit neural representations) and advanced techniques like loop closure detection and efficient point cloud processing. These advancements are crucial for enabling autonomous navigation in various applications, including robotics, autonomous driving, and 3D scene reconstruction.