Navigation & SLAM

Loop Closure

Loop closure is the recognition that a robot has returned to a previously visited place, and the incorporation of that constraint to correct accumulated drift in a SLAM trajectory and map. Detection uses appearance-based methods such as bag-of-visual-words retrieval or LiDAR descriptors like Scan Context, followed by geometric verification; the resulting constraint triggers a global pose-graph optimization that redistributes error over the trajectory.

Why it matters for physical AI

Without loop closure, odometry drift makes large maps unusable within minutes; robust place recognition is what allows robots to maintain consistent maps across building-scale spaces and multi-hour missions.