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 from multiple agents is still a challenging problem. 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 and release our code at http://curb.cs.uni-freiburg.de.

翻译：地图在实现安全与自动驾驶中扮演着不可或缺的角色。尽管从SLAM到语义理解等多个方面取得了诸多进展，但从多智能体构建城市动态场景的可操作分层语义表征仍是一项具有挑战性的问题。本研究提出协同城市场景图（CURB-SG），该框架能够支持自动驾驶多项功能的高阶推理与高效查询。CURB-SG利用多智能体的全景激光雷达数据，通过一种基于图的协同SLAM方法检测智能体间闭环，构建大规模地图。为对所得三维地图进行语义分解，我们基于自车路径及其对其他车辆的全景观测构建车道图。依据车道图的连通性，将环境划分为交叉与非交叉道路区域。继而构建包含车道信息、静态地标位置及其与特定地图区域的对应关系、自车观测到的其他车辆、以及包含三维全景点云的SLAM位姿图在内的多层场景图。我们通过高真实感模拟器在城市场景中全面评估CURB-SG，并将代码开源发布于http://curb.cs.uni-freiburg.de。