SLAM

SLAM (Simultaneous Localization and Mapping) is a computational technique that allows a robot or autonomous system to build a map of an unknown environment while simultaneously tracking its own position within that map. It solves a fundamental chicken-and-egg problem in robotics: you need a map to localize, but you need to know your location to build a map. SLAM is implemented through probabilistic algorithms — including Extended Kalman Filters, particle filters, and graph-based optimization — and can fuse data from LiDAR, cameras, IMUs, and other sensors.

Autonomous vehicles, delivery drones, warehouse robots, and AR headsets all depend on SLAM to navigate without GPS or pre-built maps. In 2026, robotics companies, self-driving vehicle startups, and AR/VR hardware makers actively recruit SLAM engineers because it sits at the intersection of real-time estimation, sensor fusion, and 3D geometry — a rare and high-value combination. The field has expanded into spatial AI, where SLAM-derived scene understanding powers foundation models for embodied agents.