UravAdvanced

ChronoSpatial turns static indoor environments into self-healing, crowdsourced 4D semantic world models for Embodied AI and Mixed Reality navigation. Traditional indoor spatial computing and AR navigation are fundamentally brittle: the moment furniture moves, an aisle is blocked, or a user walks beyond a typical 20-meter Guardian boundary, static maps break down. ChronoSpatial resolves this by merging real-time visual-inertial SLAM with Meta’s foundation AI models into a continuously adapting spatial memory. Key Capabilities: 1. Non-Blocking 2D-to-3D Semantic Lifting: As users walk, a background sidecar queue passes keyframes to Meta’s SAM 3.1 via the Meta Model API. Decoded Base85 token masks are combined with camera intrinsics and a multi-tier depth waterfall (AR plane intersections and triangulated SLAM landmark consensus) to calculate metric 3D bounding wireframes and world-space centroids without dropping SLAM tracking frames. 2. 4D Living Map Reconciler: When spaces are rescanned or synced peer-to-peer over local Wi-Fi, the engine executes temporal spatial diffing. If furniture moves by more than 0.6m, it is dynamically flagged as [MOVED], drawing a visual displacement trail and re-weighting the walkable NavMesh in real time. 3. Autonomous Waypoint Synthesis: Rather than requiring users to manually record paths, ChronoSpatial treats historical keyframe poses as a topological free-space graph. 4. Conversational Spatial Intelligence: Tapping "Ask AI" queries Meta's Muse Spark (muse-spark-1.3). Muse Spark evaluates user pose against nearby 3D semantic entities, circumvents newly detected hazards, and articulates clear navigation guidance via Text-to-Speech while rendering directional 3D arrows across the physical floor. 5. Cross-Device Campus Scale: Built with a decentralized LAN sync protocol, mobile devices act as high-resolution environmental sensors while streaming crowdsourced spatial models to Meta Quest headsets for boundaryless mixed reality passthrough.