EnhanceERASOR: Two-Stage Static 3D Point Cloud Mapping in Dynamic Scenes

Abstract

A clean map of the surrounding environment is essential for autonomous driving systems to ensure reliable localization and safe path planning. However, the existence of dynamic objects introduces ghost traces into the map, significantly degrading its quality. To address this issue, we propose EnhanceERASOR, a two-stage framework for static 3D point cloud mapping, consisting of a lightweight OnlineERASOR stage for real-time static mapping and an OfflineRefinement stage for global optimization. The Online-ERASOR stage utilizes the egocentric ratio of pseudo occupancy between consecutive scans to identify dynamic points, followed by verification and post-processing strategies to suppress false positives and false negatives. The Offline-Refinement stage introduces a submap-to-map consistency check to suppress semi-dynamic and slow-moving objects, and adopts a voxel-guided strategy for dense static mapping. Extensive experiments on diverse datasets with different scenarios and sensors demonstrate the superior performance, robustness, and generalization ability of our proposed method in static map construction.