Semantic-Guided Geometric Feature Extraction from Dense LiDAR for Vehicle Localization with Abstract Maps

Abstract. High-precision vehicle localization in GNSS-denied urban areas requires alternatives to costly HD maps. In this paper, we present a novel framework for feature extraction and benchmark generation to enable high-precision localization using abstract LoD2/DTM maps as a replacement for HD maps. Our first contribution, a semantic-geometric pipeline, processes dense LiDAR and camera data to extract map primitives. This is accomplished by a RANSAC-fitted ground plane extraction step, followed by a semantic filter that discards dynamic objects. Finally, geometric clustering (HDBSCAN) and RANSAC plane fitting isolate large-scale vertical facades. Our second contribution, a multi-stage GT generation framework, resolves annotation ambiguity using a Human-In-The-Loop (HITL) system. A robust 2D pose is computed by finding the geometric median of bootstrapped transformation samples on the S E(2) manifold, which is then refined to a 6-Degree-of-Freedom pose via point-to-plane ICP, before being validated by a human for a final check. We evaluated our feature extraction pipeline against the generated benchmark, achieving 95.04% precision and 83.74% recall. An analysis of this performance shows the pipeline correctly rejects small, ambiguous features while achieving high recall on all large, stable features, proving its suitability for a robust localization filter.