Maps have played an indispensable role in enabling safe and automated driving. Although there have been many advances on different fronts ranging from SLAM to semantics, building an actionable hierarchical semantic representation of urban dynamic scenes and processing information from multiple agents are still challenging problems. In this work, we present Collaborative URBan Scene Graphs (CURB-SG) that enable higher-order reasoning and efficient querying for many functions of automated driving. CURB-SG leverages panoptic LiDAR data from multiple agents to build large-scale maps using an effective graph-based collaborative SLAM approach that detects inter-agent loop closures. To semantically decompose the obtained 3D map, we build a lane graph from the paths of ego agents and their panoptic observations of other vehicles. Based on the connectivity of the lane graph, we segregate the environment into intersecting and non-intersecting road areas. Subsequently, we construct a multi-layered scene graph that includes lane information, the position of static landmarks and their assignment to certain map sections, other vehicles observed by the ego agents, and the pose graph from SLAM including 3D panoptic point clouds. We extensively evaluate CURB-SG in urban scenarios using a photorealistic simulator. We release our code at http://curb.cs.uni-freiburg.de.

翻译：地图在保障安全与自动驾驶中发挥着不可或缺的作用。尽管从SLAM到语义理解等诸多领域已取得显著进展，但构建城市动态场景的可操作分层语义表征，并处理来自多个智能体的信息，仍是具有挑战性的问题。本文提出协同城市场景图（CURB-SG），该框架能够支持高速公路驾驶中多种功能的高阶推理与高效查询。CURB-SG利用来自多个智能体的全景激光雷达数据，通过一种高效的基于图的协同SLAM方法构建大规模地图，该方法可检测智能体间的闭环。为对获取的三维地图进行语义分解，我们基于主车路径及其对其他车辆的全景观测构建车道图。根据车道图的连通性，将环境划分为交叉与非交叉道路区域。随后，我们构建包含车道信息、静态地标位置及其与特定地图片段的关联、主车观测到的其他车辆、以及包含三维全景点云的SLAM位姿图的多层次场景图。我们通过逼真仿真器在城市场景下对CURB-SG进行全面评估，并将代码开源至 http://curb.cs.uni-freiburg.de。