What Is SLAM? A short-and-sweet explainer
SLAM (Simultaneous Localization and Mapping) is a technology that enables autonomous systems, like robots and self-driving cars, to build a map of an unknown environment while simultaneously tracking their location within it, using sensor data such as cameras, lidar, and IMUs to navigate without GPS.
Background
SLAM stands for Simultaneous Localization and Mapping. It's a computational technique used by robots, self-driving cars, drones, and augmented-reality devices to build a map of an unknown environment while simultaneously keeping track of their own location within that map—all without GPS. The core challenge is a "chicken-and-egg" problem: to know where you are, you need a good map; to build a good map, you need to know where you are. SLAM solves both at once, typically using sensors like cameras, LiDAR, or radar. The term was coined in the robotics research community in the 1990s and is now a foundational technology in autonomous vehicles (e.g., Waymo, Tesla), robotics (Roomba, warehouse bots), and AR/VR (Apple Vision Pro, Microsoft HoloLens). This article is a short explainer from Aptiv, an auto-tech supplier that makes self-driving hardware and software—so they have a direct commercial interest in SLAM.