Autonomous driving paper index
Urban-Graph: Bridging Local SLAM and Global Earth Observation for Fine-Grained Urban LCLU Mapping
One-line summary
We present urban graph, which combines overhead EO priors, vehicle observations, and fixed roadside anchors in a hierarchical semantic scene graph.
Engineering notes
Key topics: autonomous driving, lidar, carla. See the paper for implementation details and experimental results.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
Abstract. Urban scene understanding requires both global geographic context and local structural detail. Earth Observation (EO) imagery supports large-scale land-cover and land-use (LCLU) mapping, but in urban areas it often merges heterogeneous surfaces into broad built-up classes. Vehicle-based sensors such as LiDAR and cameras recover these local structures, but their maps can drift and often remain in a local coordinate frame. We present urban graph, which combines overhead EO priors, vehicle observations, and fixed roadside anchors in a hierarchical semantic scene graph. Coarse georeferenced regions from EO data are updated with local observations, while a factor graph jointly optimises SLAM constraints and global geodetic constraints. The resulting graph is projected back to the overhead layer to separate coarse urban classes into finer semantic components. Experiments in CARLA show improved global alignment, reduced drift, and more detailed projection of local semantics into EO space.
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