Collaborative Dynamic 3D Scene Graphs for Automated Driving

TL;DR

This paper introduces a collaborative 3D scene graph framework for automated driving that integrates multi-agent LiDAR data, semantic decomposition, and hierarchical mapping to improve urban scene understanding and reasoning.

Contribution

It presents a novel multi-layered scene graph approach that combines collaborative SLAM, semantic lane decomposition, and multi-agent data integration for urban environments.

Findings

Effective multi-agent SLAM with inter-agent loop closure detection

Hierarchical scene graph construction including lanes, landmarks, and vehicles

Extensive evaluation in urban scenarios demonstrates improved scene understanding

Abstract

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.