Navigation & SLAM

Graph-Based SLAM

Graph-based SLAM is a formulation of simultaneous localization and mapping as inference over a graph whose nodes are robot poses (and optionally landmarks) and whose edges encode relative measurements from odometry, scan matching, or loop closures. The map and trajectory are recovered by nonlinear least-squares optimization over the graph, as in g2o, Ceres, or GTSAM backends. It superseded filter-based SLAM for most applications due to accuracy and scalability.

Why it matters for physical AI

Consistent long-term maps require correcting drift when places are revisited, and graph optimization is the machinery that makes city-scale, lifelong mapping computationally feasible on deployed robots.