Navigation & SLAM
Localization
Localization is the problem of estimating a robot's pose within a known map or reference frame from sensor data. Probabilistic approaches dominate: particle-filter methods such as adaptive Monte Carlo localization (AMCL) match LiDAR scans against occupancy grids, while Kalman-filter variants fuse GPS, IMU, and odometry outdoors. Global localization additionally resolves the kidnapped-robot problem of initializing pose without a prior estimate.
Why it matters for physical AI
Every downstream navigation and mobile manipulation decision inherits localization error; centimeter-level, drift-free pose estimates are what let robots repeatably dock, pick from shelves, and operate through long unattended shifts.
Build physical AI
Put these concepts to work on real hardware
Axol is a dual-arm robot built for physical AI — teleoperate it, collect demonstrations, and deploy learned policies out of the box.