Navigation & SLAM

SLAM

SLAM (Simultaneous Localization and Mapping) is the problem of building a map of an unknown environment while concurrently estimating the robot's pose within it, using sensors such as LiDAR, cameras, and IMUs. Solutions evolved from EKF-SLAM and particle filters to modern graph-based optimization over poses and landmarks, with loop closure correcting accumulated drift. ORB-SLAM, LIO-SAM, and Cartographer are widely used systems.

Why it matters for physical AI

Mobile robots cannot presume known maps or external positioning; SLAM supplies the spatial self-awareness on which navigation, exploration, and mobile manipulation are built.