SLAM Systems
xtan explores SLAM workflows where stereo vision, spatial tracking, and geometry-aware sensing support simultaneous localization and mapping in dynamic real-world environments.
Simultaneous localization and mapping
SLAM (Simultaneous Localization and Mapping) systems allow machines to estimate their position while building a map of the surrounding environment. These systems are widely used in robotics, autonomous systems, XR platforms, and spatial computing environments.
Potential for robotics and navigation systems
SLAM algorithms combine visual sensing, motion estimation, and spatial mapping to understand unknown environments. Stereo vision pipelines may support experimental perception workflows where spatial tracking contributes to navigation, mapping, and environment awareness.
Why xtan can be relevant
xtan focuses on stereo vision, geometry-first interaction, and practical spatial systems. Within SLAM workflows this may support experimental perception pipelines, spatial mapping systems, and research into geometry-driven localization and navigation.