Navigation & SLAM
Kalman Filter
The Kalman filter is a recursive state estimator, introduced by Rudolf Kalman in 1960, that optimally fuses a linear dynamics prediction with noisy measurements under Gaussian assumptions, alternating predict and update steps. The extended (EKF) and unscented (UKF) variants handle nonlinear systems by linearization or sigma-point propagation, and Kalman-family filters remain the standard tool for fusing IMU, odometry, GPS, and vision.
Why it matters for physical AI
Nearly every robot's belief about where it is and how fast it is moving flows through a Kalman-style filter, making estimation quality a silent ceiling on control and navigation performance.
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.