SLAM: Difference between revisions

[[SLAM]] ('''S'''imultaneous '''L'''ocalization '''A'''nd '''M'''apping) is a computational problem and a set of [[algorithms]] used primarily in robotics and autonomous systems, including [[VR headset]]s and [[AR headset]]s. The core challenge SLAM addresses is often described as a "chicken-and-egg problem": to know where you are, you need a map, but to build a map, you need to know where you are. SLAM solves this by enabling a device, using data from its onboard [[sensors]] (like [[cameras]], [[IMU]]s, and sometimes [[depth sensors]] like [[Time-of-Flight|Time-of-Flight (ToF)]]), to construct a [[map]] of an unknown [[environment]] while simultaneously determining its own position and orientation ([[pose]]) within that newly created map. This self-contained process enables [[inside-out tracking]], meaning the device tracks its position in [[3D space]] without needing external sensors or markers (like [[Lighthouse]] base stations).