LINK: Learning to Localize from Known to Unknown Scenes

LiDAR relocalization based on scene coordinate regression (SCR) degrades sharply when test trajectories traverse route segments absent from training, a practical challenge in long-range autonomous-driving deployment, where complete route coverage is rarely achievable. We present LINK, a confidence-guided framework for relocalization under partial scene coverage. LINK predicts an absolute pose hypothesis and scene features with an SCR backbone, estimates pose reliability from temporal pose-feature sequences, and uses a decision policy to arbitrate between direct absolute hypotheses and relative motion propagated from reliable historical estimates, thereby preserving globally referenced localization across uncovered segments. We also introduce UrbanUnseen, a city-scale benchmark with native known-to-unknown scene transitions, together with controlled partial-coverage protocols on Oxford and NCLT. Experiments show that LINK substantially improves robustness when training coverage is incomplete, consistently outperforms representative map-free baselines under partial coverage, and remains effective in fully known scenes.