Simultaneous localization and mapping (SLAM) is the core capability that allows robots and autonomous systems to build a map of an unknown environment while estimating their own motion within it. This introductory talk explains what SLAM is, why it matters, how it works and where it shows up in real products—from mobile robots and drones to AR/VR and automotive. We’ll walk through a conceptual formulation and block diagram of a modern SLAM system, define key terminology and describe the main components (front-end matching, state estimation, map management and loop closure) with minimal math. The session highlights practical design trade-offs such as compute versus accuracy, common algorithmic failure modes (dynamic scenes, sensor motion, degraded features) and implementation challenges in real-time systems. We’ll conclude with sensor options and multimodal fusion, a visual example of SLAM in action and a survey of off-the-shelf libraries and stacks that teams can adapt quickly.