Short Loop Closures Repaired a Long-Lived Map

Robots revisiting a site can accumulate maps whose local trajectories look sound while global alignment drifts across days or platforms. Chain-SLAM initializes session alignment from GNSS proximity, detects loop closures online and propagates their geometric constraints through an adjacency graph. Loaded maps and new trajectories are then optimized inside one factor graph, preserving both inter-session and intra-session consistency without requiring dynamic-object removal. The authors report improved trajectory accuracy and robust large-scale integration and release the source code. Performance remains tied to the tested datasets, place-recognition assumptions and available loop closures.