DIAL: Drift-aware Continual Alignment for Implicit LiDAR Localization

Implicit LiDAR localizers compress a scene into a metric field stored in network parameters, but this field becomes stale as environments evolve. Repeated pose annotation and full retraining are costly across recurring revisits. Continual updates face two coupled problems: supervision is missing where the historical model fails, and unconstrained adaptation can overwrite a coordinate readout that remains valid elsewhere. We present the first study of continual learning for implicit LiDAR localization. We find that the historical localizer supports both needs: its predictions reveal where the implicit map has changed, while its metric readout preserves valid coordinate associations when old scans are unavailable. DIAL’s Temporal Consistency-guided Pseudo-label Pipeline (TCPP) cross-validates retrieval and scene-coordinate evidence over time to recover pseudo poses for changed or unseen frames. Head-Decoupled Encoder Regularization (HDER) freezes the readout and adapts selected encoder blocks when old scans are inaccessible. With historical memory, Memory-Conditioned Rehearsal (MCR) reinstates old-scene constraints; Redundancy-Aware Exemplar Replay (RAER) selects the replay needed as route coverage expands. On NCLT, pseudo labels for about 20% of revisit frames suffice for maintenance. A buffer-based DIAL route updates the localizer for the first NCLT environmental change in about 12 minutes of continual optimization; repeated maintenance then sustains localization across roughly one year of NCLT revisits, while segmented QEOxford approaches full-retraining accuracy. Code will be released.