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Collaborative Dynamic 3D Scene Graphs for Automated Driving
Open accessAbstract
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.
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3D scene graphs allow efficient representation of larger scenes.
“By representing objects or spatial concepts as nodes and their relations as edges, 3D scene graphs allow to efficiently represent larger scenes [36, 18, 19].”
From Hierarchical Open-Vocabulary 3D Scene Graphs for Language-Grounded Robot Navigation (2024) · cites this paper as [18]
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Metadata
- Authors
- Greve, E., Büchner, M., Vödisch, N., Burgard, W., & Valada, A.
- Affiliations
- University of Freiburg (DE)
- University of Technology Nuremberg (DE)
- Venue
- 2024 IEEE International Conference on Robotics and Automation (ICRA)
- Publisher
- IEEE
- Published
- 2024-05-13
- Pages
- 11118-11124
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W4401415891
- Cited by
- 32
- References
- 41
- Citations by year
- 2026: 12 · 2025: 12 · 2024: 8
- FWCI
- 26.16
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Proceedings - IEEE International Conference on Robotics and Automation/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- FreiDok plus (Universitätsbibliothek Freiburg) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotics and Sensor-Based Localization
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Automated Road and Building Extraction
- Keywords
- scene graph, collaborative SLAM, autonomous driving, loop closure detection, pose graph, 3D point clouds, higher-order reasoning
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