SLAM, or Simultaneous Localization and Mapping, is a fundamental problem in AI robotics where a robot needs to build a map of an unknown environment while simultaneously tracking its own position within that map. This intricate process is often described as a chicken-and-egg problem, as accurate localization requires an existing map, and an accurate map can only be built with precise knowledge of the robot's location. Robots achieve SLAM by integrating data from various sensors like cameras, lidar, and inertial measurement units (IMUs) to constantly refine both the map and their pose. It is a critical capability enabling autonomous navigation, exploration, and interaction for robots operating in dynamic and unstructured settings, without prior knowledge of their surroundings. More details: https://course.cpi-nis.kz/Home/SetCulture?backurl=https://infoguide.com.ua/&culture=ru-ru