SLAM (Simultaneous Localization and Mapping)

SLAM addresses the chicken-and-egg problem: to localize, you need a map, but to build a map, you need to know your position. The algorithm maintains both simultaneously. As the robot moves, sensors (LiDAR, cameras) detect environmental features - corners, edges, distinctive objects. These observations are matched to previously seen features to estimate how far the robot moved. This movement estimate updates the robot's position on the growing map. New features are added to the map at their calculated positions. Loop closure detection recognizes when the robot returns to previously mapped areas, allowing the system to correct accumulated errors. Graph optimization techniques refine both the map and the position history to maintain consistency.