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.
Details
- Organisationseinheit(en)
-
Geodätisches Institut
- Externe Organisation(en)
-
Quality Match GmbH
- Typ
- Konferenzaufsatz in Fachzeitschrift
- Journal
- ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
- Band
- 11
- Seiten
- 25-33
- Anzahl der Seiten
- 9
- ISSN
- 2194-9042
- Publikationsdatum
- 03.07.2026
- Publikationsstatus
- Veröffentlicht
- Peer-reviewed
- Ja
- ASJC Scopus Sachgebiete
- Instrumentierung, Umweltwissenschaften (sonstige), Erdkunde und Planetologie (sonstige)
- Elektronische Version(en)
-
https://doi.org/10.5194/isprs-annals-XI-1-2026-25-2026 (Zugang:
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)
