InterLoc: LiDAR-based Intersection Localization using Road Segmentation with Automated Evaluation Method

Intersections are geometric and functional key points in every road network. They offer strong landmarks to correct GNSS dropouts and anchor new sensor data in up-to-date maps. Despite that importance, intersection detectors either ignore the rich semantic information already computed onboard or depend on scarce, hand-labeled intersection datasets. To close that gap, this paper presents a LiDAR-based method for intersection detection that (i) fuses semantic road segmentation with vehicle localization to detect intersection candidates in a bird's eye view (BEV) representation and (ii) refines those candidates by analyzing branch topology with a least squares formulation. To evaluate our method, we introduce an automated benchmarking pipeline that pairs detections with OpenStreetMap (OSM) intersection nodes using precise GNSS/INS ground-truth poses. Tested on eight SemanticKITTI sequences, the approach achieves a mean localization error of 1.9 m, 89% precision, and 77% recall at a 5 m tolerance, outperforming the latest learning-based baseline. Moreover, the method is robust to segmentation errors higher than those of the benchmark model, demonstrating its applicability in the real world.