Layer Assignment for Long-Term LiDAR Map Maintenance Using Geometric and Semantic Evidence

Long-term LiDAR maps support urban remote sensing, infrastructure inventories, change analysis, digital twins, and map-based localization. Most map-cleaning pipelines, however, output a single retained map, forcing transient observed-dynamic artifacts and movable but currently stationary scene content into the same keep/remove decision. This paper formulates layer assignment within long-term LiDAR map maintenance as a layered representation problem. The resulting output comprises a long-term static (LTS) layer, a potentially dynamic (PD) layer, and a removed observed-dynamic (OD) set. For geometry-only accumulated maps with per-frame semantic predictions, the proposed layer-assignment framework combines map-to-scan geometric inconsistency, dual-timescale map-side semantic memory, PD admission, and PD-preserving recovery within the geometric rejection set. Geometric evidence first identifies OD candidates and rejection regions, while semantic memory converts framewise class predictions into map-side movability evidence for LTS/PD assignment. Experiments on SemanticKITTI under a controlled in-sequence LTS/PD/OD protocol show that this framework maintains high LTS-layer retention, improves PD retention, and reduces PD over-cutting while maintaining competitive OD suppression with predicted semantic input. Mechanism analyses further show that the dual-timescale semantic memory helps preserve PD evidence under conflicting or temporally sparse semantic predictions, while PD-preserving recovery provides boundary compensation with an explicit OD-suppression trade-off. Collapsing the output to a single retained map also improves the balance between static and PD retention relative to single-layer cleaning baselines. Qualitative Apollo SouthBay transfer observations further indicate that the same configuration can produce a three-layer map output without retuning, with binary annotations used only to support map-quality checks.