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Open-Set 3D Scene Graphs for Field Robotics: An Outdoor Case Study

Summary

This field report, accepted by the IEEE Transactions on Field Robotics, analyzes 3D scene graph (3DSG) behavior in complex outdoor settings using the Terra 3DSG case study across five outdoor robotic datasets. It introduces novel consistency metrics for repeated traversals and finds that VLM point embeddings often contain outliers and multiple semantic modes. Navigation-based object retrieval reaches about 70% success with only ~66% average path efficiency, while region-level understanding F1 is around 0.359. The authors also show that compact (<600MB) and relatively consistent maps are achievable for multi-kilometer outdoor trajectories, while highlighting key open challenges.

SourcearXiv RoboticsAuthor: Chad R. Samuelson, Gabriel R. Slade, Joshua G. Mangelson
Open-Set 3D Scene Graphs for Field Robotics: An Outdoor Case Study
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[Submitted on 4 Sep 2026]

Title:Open-Set 3D Scene Graphs for Field Robotics: An Outdoor Case Study

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Abstract:Three-dimensional scene graphs (3DSGs) have emerged as a promising approach for building geometrically grounded, semantically informed, hierarchical general-purpose maps to support high-level robotic reasoning. However, the behavior of 3DSGs in real-world outdoor deployments remains poorly understood, particularly when combined with open-set vision-language models (VLMs). In this field report, we analyze the components common to most 3DSG representations across five outdoor robotic datasets to characterize challenges that arise in complex outdoor environments. Using the recently proposed Terra 3DSG as a case study, we investigate semantic point embeddings, place-node graph navigation, region-level understanding, and memory size across the five diverse datasets. We additionally introduce novel consistency metrics to evaluate whether semantic and structural graph properties remain stable across repeated traversals of the same environment. Our analysis reveals that outliers and multiple modes are common in VLM point embeddings across all tested datasets with outlier ratios above $0.1$ for around $30\%$ of points. We demonstrate the feasibility of outdoor 3DSGs for navigation-based object retrieval, achieving success rates near $70\%$, though performance is limited by traversability failures and inefficient routing, with trajectories averaging approximately $66\%$ suboptimal path efficiency. Region-level understanding remains challenging in complex natural environments, with low average F1 scores around $0.359$. Overall, our results show that outdoor 3DSGs can maintain compact (less than $600$MB for multi-kilometer trajectories) and relatively consistent large-scale environment representations, while highlighting open challenges in handling multiple semantic modes, incorporating traversability into graph structures, and improving higher-level region understanding.

Comments: This work has been accepted for publication with the IEEE Transactions of Field Robotics Journal

Subjects:

Robotics (cs.RO)

Cite as: arXiv:2609.04607 [cs.RO]

(or arXiv:2609.04607v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.04607

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Chad Samuelson [view email] [v1] Fri, 4 Sep 2026 01:14:09 UTC (30,189 KB)

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Key points and analysis

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Key points

  • Across five outdoor datasets, VLM point embeddings frequently contain outliers and multiple semantic modes; roughly 30% of points have outlier ratios above 0.1.
  • Navigation-based object retrieval with Terra 3DSG succeeds in about 70% of attempts, but average trajectories are only about 66% of optimal path efficiency due to traversability and routing failures.
  • Region-level understanding remains difficult in natural environments, with average F1 around 0.359.
  • Outdoor 3DSGs can maintain compact (<600MB for multi-kilometer trajectories) and relatively consistent large-scale environment representations.

Highlights and analysis are generated automatically and may contain errors. Check the original source.

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