Navigation & SLAM
Bundle Adjustment
Bundle adjustment is the joint nonlinear least-squares optimization of camera poses and 3D landmark positions to minimize reprojection error across many images, the gold-standard refinement step in structure from motion and visual SLAM. Its sparse structure is exploited via the Schur complement and solvers such as Ceres, g2o, and GTSAM, and keyframe-based systems like ORB-SLAM run local and global bundle adjustment to maintain map consistency.
Why it matters for physical AI
Accurate maps and camera poses from bundle adjustment underpin robot localization, 3D reconstruction for digital twins, and the camera-pose ground truth used to train and evaluate spatially aware perception models.
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.