SLAM
SLAM (Simultaneous Localization and Mapping) is a computational technique used in robotics and autonomous systems to build a map of an unknown environment while simultaneously tracking the system's location within it. It enables robots, drones, and augmented reality devices to navigate and interact with their surroundings without prior knowledge of the environment. The process typically involves sensor data from cameras, LiDAR, or IMUs to estimate poses and create consistent spatial representations.
Developers should learn SLAM when working on autonomous vehicles, robotics, drones, or augmented/virtual reality applications that require real-time spatial awareness and navigation. It is essential for tasks like indoor robot navigation, self-driving car localization, and AR object placement in physical spaces, as it allows systems to operate in dynamic, unstructured environments without relying on external infrastructure like GPS.