Simultaneous Localization and Mapping (SLAM)

Simultaneous Localization and Mapping (SLAM) is a computational problem in robotics and computer vision where a device, such as a robot or a drone, must create a map of an unknown environment while simultaneously keeping track of its own location within that environment. The main characteristics of SLAM include the integration of sensor data, such as LiDAR or camera inputs, to build a spatial representation of the surroundings. SLAM algorithms are commonly used in autonomous vehicles, robotics, augmented reality, and navigation systems. By enabling real-time mapping and localization, SLAM is crucial for applications that require a device to operate in dynamic, unstructured environments.