标题： InterKey：用于OpenStreetMap全局定位的跨模态交点关键点 显示英文标题

标题： InterKey: Cross-modal Intersection Keypoints for Global Localization on OpenStreetMap

摘要： 可靠的全局定位对于自动驾驶车辆至关重要，特别是在GNSS退化或不可用的环境中，例如城市峡谷和隧道。 尽管高清（HD）地图提供了准确的先验信息，但数据收集、地图构建和维护的成本限制了其可扩展性。 OpenStreetMap（OSM）提供了一个免费且全球可用的替代方案，但其粗略的抽象给与传感器数据的匹配带来了挑战。 我们提出 InterKey，一种跨模态框架，利用道路交叉口作为独特的地标进行全局定位。 我们的方法通过联合编码点云和OSM中的道路和建筑印记来构建紧凑的二进制描述符。 为了弥合模态差距，我们引入了差异缓解、方向确定和面积均衡采样策略，实现了鲁棒的跨模态匹配。 在KITTI数据集上的实验表明， InterKey达到了最先进的准确性，大大优于最近的基线方法。 该框架可以推广到能够生成密集结构点云的传感器，为鲁棒的车辆定位提供了一种可扩展且成本效益高的解决方案。 显示英文摘要

摘要： Reliable global localization is critical for autonomous vehicles, especially in environments where GNSS is degraded or unavailable, such as urban canyons and tunnels. Although high-definition (HD) maps provide accurate priors, the cost of data collection, map construction, and maintenance limits scalability. OpenStreetMap (OSM) offers a free and globally available alternative, but its coarse abstraction poses challenges for matching with sensor data. We propose InterKey, a cross-modal framework that leverages road intersections as distinctive landmarks for global localization. Our method constructs compact binary descriptors by jointly encoding road and building imprints from point clouds and OSM. To bridge modality gaps, we introduce discrepancy mitigation, orientation determination, and area-equalized sampling strategies, enabling robust cross-modal matching. Experiments on the KITTI dataset demonstrate that InterKey achieves state-of-the-art accuracy, outperforming recent baselines by a large margin. The framework generalizes to sensors that can produce dense structural point clouds, offering a scalable and cost-effective solution for robust vehicle localization.