Navigation & SLAM
Information Matrix
The information matrix is the inverse of a covariance matrix, representing the certainty of a Gaussian estimate; large entries indicate strong constraints between the corresponding variables. Graph-based SLAM exploits the natural sparsity of the information form, since each measurement links only a few poses and landmarks, enabling efficient factor-graph solvers such as g2o and GTSAM to optimize millions of variables.
Why it matters for physical AI
Sparse information-form representations are why modern SLAM scales to long deployments, letting robots maintain consistent maps and pose estimates over hours of operation on modest onboard compute.
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.