Simultaneous Localization and Mapping (SLAM)

Simultaneous Localization and Mapping (SLAM) is a family of algorithms that allow devices to build a map of an unknown environment while tracking their own pose within it. SLAM underpins Spatial AI, Spatial GPS, and most AR Spatials and robotics systems. As a headset or robot moves, SLAM fuses sensor data into a growing Spatial map and continuously refines past estimates. This enables stable Spatial anchors, VR Spatials twins constructed from real scans, and multiuser experiences that share a coordinate frame. Platforms like Spatials.aiTM often integrate SLAM outputs from many devices into a consolidated Spatial data layer, turning ad-hoc local maps into persistent, multi-session Spatials.