Simultaneous Localization and Mapping (SLAM) is a technique used by robots and autonomous vehicles to build a map of an unknown environment while simultaneously keeping track of its current location. The SLAM problem involves two key steps - mapping and localization - which are paradoxical, as mapping requires knowing the location and determining the location requires having a map. The SLAM process uses features extracted from sensor data like laser scans to repeatedly update estimates of landmark positions and the robot's location via an Extended Kalman Filter as the robot moves through the environment.