InterLoc: LiDAR-based Intersection Localization using Road Segmentation with Automated Evaluation Method
Online localization of road intersections is beneficial for autonomous vehicle localization, mapping and motion planning. Intersections offer strong landmarks to correct vehicle pose estimation in GNSS dropouts and anchor new sensor data in up-to-date maps. They are also decisive routing nodes in road network graphs. Despite that importance, intersection localization has not been widely studied, with existing methods 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 online vehicle-centric intersection localization. We fuse semantic road segmentation with vehicle local pose to detect intersection candidates in a bird's eye view (BEV) representation. We then refine those candidates by analyzing branch topology and correcting the intersection point in a least squares formulation. To evaluate our method, we introduce an automated benchmarking pipeline that pairs localized intersection points with OpenStreetMap (OSM) intersection nodes using precise GNSS/INS ground-truth poses. Experiments on SemanticKITTI show that the method outperforms the latest learning-based baseline in accuracy and reliability. Moreover, sensitivity tests demonstrate that our method is robust to challenging segmentation error levels, highlighting its applicability in the real world.
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BenchmarkingMotion PlanningPose EstimationRoad SegmentationVehicle Pose EstimationSimilar Papers 제목 키워드 기반
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