InterLoc：基于LiDAR的交叉口定位，使用道路分割与自动评估方法
InterLoc: LiDAR-based Intersection Localization using Road Segmentation with Automated Evaluation Method
AI总结：
提出基于LiDAR的在线交叉口定位方法InterLoc，通过BEV语义道路分割检测并细化交叉口候选点，结合自动化评估流水线，在SemanticKITTI上实现了优于基线的准确性与鲁棒性。
摘要：
道路交叉口的在线定位对自动驾驶车辆的定位、建图和运动规划十分有益。交叉口提供了强地标，可用于纠正车辆位姿估计，在最新地图中锚定新传感器数据，并指导道路网络图中的车辆路由。尽管如此重要，交叉口定位尚未被广泛研究，现有方法要么忽略车载已计算的丰富语义信息，要么依赖稀缺的人工标注交叉口数据集。为填补这一空白，我们提出一种基于LiDAR的在线以车辆为中心的交叉口定位方法。我们在由拼接一系列语义道路扫描形成的鸟瞰图（BEV）表示中检测交叉口候选。随后，通过分析相交道路分支并在最小二乘公式中调整交叉口中心点来细化这些候选。在评估方面，我们引入一个自动化流水线，利用精确的GNSS/INS真值位姿将定位的交叉口点与OpenStreetMap（OSM）交叉口节点配对。在SemanticKITTI数据集上的实验表明，我们的方法在准确性和可靠性上优于最新基于学习的基线。敏感性测试证明了该方法对具有挑战性的分割误差的鲁棒性，突显了其在现实世界中的适用性。
英文摘要：
Online localization of road intersections is beneficial for autonomous vehicle localization, mapping and motion planning. Intersections offer strong landmarks for correcting vehicle pose estimation, anchoring new sensor data in up-to-date maps, and guiding vehicle routing in road network graphs. Despite this importance, intersection localization has not been widely studied, with existing methods either ignoring the rich semantic information already computed onboard or relying on scarce, hand-labeled intersection datasets. To close this gap, we present a novel LiDAR-based method for online vehicle-centric intersection localization. We detect the intersection candidates in a bird's eye view (BEV) representation formed by concatenating a sequence of semantic road scans. We then refine these candidates by analyzing the intersecting road branches and adjusting the intersection center point in a least-squares formulation. For evaluation, we introduce an automated pipeline that pairs localized intersection points with OpenStreetMap (OSM) intersection nodes using precise GNSS/INS ground-truth poses. Experiments on the SemanticKITTI dataset show that our method outperforms the latest learning-based baseline in accuracy and reliability. Sensitivity tests demonstrate the method's robustness to challenging segmentation errors, highlighting its applicability in the real world.
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