Navigation & SLAM
Factor Graph
A factor graph is a bipartite probabilistic graphical model that connects variable nodes, such as robot poses and landmarks, to factor nodes encoding measurement constraints between them. Factor graphs are the dominant formulation for modern SLAM and state estimation, solved via nonlinear least-squares smoothing with libraries like GTSAM and incremental solvers such as iSAM2. They generalize pose graphs by supporting arbitrary sensor factors, including IMU preintegration, GPS, and visual landmarks.
Why it matters for physical AI
Robots operating in unstructured environments must fuse many noisy sensors into a consistent state estimate; factor graphs provide the scalable, principled inference backbone for localization, mapping, and calibration on deployed systems.
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.