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arxiv: 2604.25404 · v1 · submitted 2026-04-28 · 💻 cs.RO

Robust Graph Matching through Semantic Relationship Generation for SLAM

David Perez-Saura , Jose Andres Millan-Romera , Miguel Fernandez-Cortizas , Holger Voos , Pascual Campoy , Jose Luis Sanchez-Lopez This is my paper

Pith reviewed 2026-05-07 15:50 UTC · model grok-4.3

classification 💻 cs.RO
keywords graph matchingsemantic relationsSLAMscene graphsindoor environmentsRGB-Dsymmetric layoutslocalization
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The pith

Semantic relations between objects and structural elements enable robust graph matching for SLAM in symmetric indoor environments.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper seeks to solve the problem of ambiguous correspondences in graph matching for localization when environments have repetitive or symmetric structures. Purely geometric methods struggle because many possible alignments fit the data, leading to slow or failed matching. The approach adds semantic information by detecting objects in RGB-D data and connecting them to elements like rooms and walls, using these links to eliminate inconsistent matches early. If successful, this makes localization faster and more reliable in real buildings where symmetry is common.

Core claim

The authors establish that integrating semantic relations between detected objects and structural elements such as rooms and wall planes into the graph allows filtering of candidate correspondences before geometric verification, which reduces the number of candidates, improves efficiency, and speeds convergence particularly when layouts are symmetric and geometric cues are insufficient.

What carries the argument

The semantic-enhanced filtering of candidate matches using object-to-structural-element relations prior to geometric checks in scene graph matching.

If this is right

  • Semantic filtering significantly reduces the number of candidate matches in graph matching.
  • Computational efficiency improves due to fewer candidates to check.
  • The method enables faster convergence in symmetric scenarios where geometric approaches fail.
  • Integration into frameworks like iS-Graphs supports practical SLAM applications in structured indoors.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Extending this to handle moving objects or changing environments could broaden its use in dynamic settings.
  • Combining with other sensors might further reduce dependence on accurate object detection.
  • Applying similar semantic pruning in outdoor or large-scale mapping could address ambiguity in repetitive terrains.
  • Evaluating on real robot data with ground truth would test if the simulated gains hold in practice.

Load-bearing premise

Object detections from RGB-D data and their relations to structural elements are reliable enough to filter matches without adding new errors or missing valid ones.

What would settle it

Running the method on symmetric indoor scenes with deliberately noisy or failed object detections and measuring if matching accuracy drops below the geometric baseline.

Figures

Figures reproduced from arXiv: 2604.25404 by David Perez-Saura, Holger Voos, Jose Andres Millan-Romera, Jose Luis Sanchez-Lopez, Miguel Fernandez-Cortizas, Pascual Campoy.

Figure 1
Figure 1. Figure 1: Semantic-enriched scene graph showing detected view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the proposed semantic-enhanced graph matching framework. An S-Graph is generated online, while a view at source ↗
Figure 3
Figure 3. Figure 3: Samples of simulated layouts with rectangular rooms, view at source ↗
Figure 5
Figure 5. Figure 5: Comparison of the number of solutions found by view at source ↗
read the original abstract

Graph-based representations such as Scene Graphs enable localization in structured indoor environments by matching a locally observed graph, constructed from sensor data, to a prior map. This process is particularly challenging in environments with repetitive or symmetric layouts, where structural cues alone are often insufficient to resolve ambiguities. We propose a semantic-enhanced graph matching approach that explicitly models relations between detected objects and structural elements, such as rooms and wall planes. Objects are detected from RGB-D data and integrated into the graph, and their relations to structural elements are exploited to filter candidate correspondences prior to geometric verification, significantly reducing ambiguity and search complexity. The proposed method is integrated within the iS-Graphs framework and evaluated in synthetic and simulated environments. Results show that semantic relations significantly reduce the number of candidate matches, improve computational efficiency, and enable faster convergence, particularly in symmetric scenarios where purely geometric approaches fail.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The paper proposes a semantic-enhanced graph matching approach for SLAM in structured indoor environments with repetitive or symmetric layouts. It detects objects from RGB-D data, integrates them into scene graphs, and exploits explicit semantic relations to structural elements (rooms, wall planes) to filter candidate correspondences before geometric verification. The method is integrated into the iS-Graphs framework and evaluated in synthetic and simulated environments, with claims that semantic filtering reduces candidate matches, improves efficiency, and enables faster convergence where purely geometric methods fail.

Significance. If the empirical outcomes hold, the approach offers a practical way to address a known failure mode in graph-based SLAM by leveraging readily available semantic cues to prune search spaces without introducing new errors. This could improve robustness in real indoor settings while maintaining compatibility with existing frameworks like iS-Graphs. The focus on symmetric scenarios and the use of relations to structural elements are well-motivated and could generalize to other graph-matching tasks in robotics.

major comments (1)
  1. Abstract and evaluation description: the central claims of significantly reduced candidate matches, improved computational efficiency, and faster convergence are presented without any reported metrics, baselines, error bars, or statistical details, making it impossible to assess the magnitude or reliability of the improvements from the available text.
minor comments (2)
  1. Clarify the exact definition and generation process for semantic relations (e.g., how object-to-room or object-to-plane relations are extracted and represented in the graph) to allow reproducibility.
  2. The weakest assumption—that object detections and relations from RGB-D are reliable and sufficiently discriminative—should be supported with at least qualitative failure cases or sensitivity analysis in the evaluation section.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback and the recommendation for minor revision. We address the single major comment below regarding the presentation of quantitative results.

read point-by-point responses

  1. Referee: Abstract and evaluation description: the central claims of significantly reduced candidate matches, improved computational efficiency, and faster convergence are presented without any reported metrics, baselines, error bars, or statistical details, making it impossible to assess the magnitude or reliability of the improvements from the available text.

    Authors: We agree that the abstract and evaluation summary would benefit from explicit quantitative details to substantiate the claims. The full evaluation section in the manuscript contains the underlying experimental results (including comparisons in synthetic and simulated symmetric environments), but these were summarized qualitatively in the abstract. In the revised version we will update the abstract to report specific metrics such as the observed reduction in candidate correspondences (as a percentage), runtime savings, and convergence iteration counts, along with direct comparisons to the geometric baseline. We will also add error bars and note the number of trials where appropriate in the evaluation description to improve clarity and allow assessment of reliability. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper describes an empirical semantic-enhanced graph matching method for SLAM, integrated into the iS-Graphs framework and evaluated on synthetic/simulated data. No equations, derivations, or first-principles claims appear in the provided abstract or reader's summary. Central claims about reduced candidate matches and faster convergence are presented as experimental outcomes of semantic filtering rather than quantities derived from fitted parameters or self-referential definitions. No self-citation chains, ansatzes, or uniqueness theorems are invoked as load-bearing steps. The derivation chain is therefore self-contained as a practical algorithmic proposal with external validation.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no explicit free parameters, axioms, or invented entities; the approach implicitly assumes reliable object detection and relation extraction from RGB-D, but these are not formalized or evidenced here.

pith-pipeline@v0.9.0 · 5462 in / 1100 out tokens · 54715 ms · 2026-05-07T15:50:04.409443+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

19 extracted references · 19 canonical work pages

  1. [1]

    3d scene graph: A structure for unified semantics, 3d space, and camera,

    I. Armeni, Z.-Y . He, J. Gwak, A. R. Zamir, M. Fischer, J. Malik, and S. Savarese, “3d scene graph: A structure for unified semantics, 3d space, and camera,” inProceedings of the IEEE/CVF international conference on computer vision, 2019, pp. 5664–5673

    work page 2019

  2. [2]

    S- graphs+: Real-time localization and mapping leveraging hierarchical representations,

    H. Bavle, J. L. Sanchez-Lopez, M. Shaheer, J. Civera, and H. V oos, “S- graphs+: Real-time localization and mapping leveraging hierarchical representations,”IEEE Robotics and Automation Letters, vol. 8, no. 8, pp. 4927–4934, 2023

    work page 2023

  3. [3]

    Graph-based global robot localization inform- ing situational graphs with architectural graphs,

    M. Shaheer, J. A. Millan-Romera, H. Bavle, J. L. Sanchez-Lopez, J. Civera, and H. V oos, “Graph-based global robot localization inform- ing situational graphs with architectural graphs,” in2023 International Conference on Intelligent Robots and Systems, pp. 9155–9162

    work page

  4. [4]

    3D Dynamic Scene Graphs: Actionable Spatial Perception with Places, Objects, and Humans,

    A. Rosinol, A. Gupta, M. Abate, J. Shi, and L. Carlone, “3D Dynamic Scene Graphs: Actionable Spatial Perception with Places, Objects, and Humans,” inProceedings of Robotics: Science and Systems, Corvalis, Oregon, USA, July 2020

    work page 2020

  5. [5]

    Kimera: an open- source library for real-time metric-semantic localization and mapping,

    A. Rosinol, M. Abate, Y . Chang, and L. Carlone, “Kimera: an open- source library for real-time metric-semantic localization and mapping,” in2020 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2020, pp. 1689–1696

    work page 2020

  6. [6]

    Hughes, Y

    N. Hughes, Y . Chang, and L. Carlone, “Hydra: A real-time spatial perception system for 3d scene graph construction and optimization,” arXiv preprint arXiv:2201.13360, 2022

    work page arXiv 2022

  7. [7]

    Khronos: A unified approach for spatio-temporal metric-semantic slam in dynamic environments,

    L. Schmid, M. Abate, Y . Chang, and L. Carlone, “Khronos: A unified approach for spatio-temporal metric-semantic slam in dynamic environments,”arXiv preprint arXiv:2402.13817, 2024

    work page arXiv 2024

  8. [8]

    Data association for mobile robot navigation: A graph theoretic approach,

    T. Bailey, E. M. Nebot, J. Rosenblatt, and H. F. Durrant-Whyte, “Data association for mobile robot navigation: A graph theoretic approach,” inProceedings 2000 ICRA. Millennium Conference. IEEE Interna- tional Conference on Robotics and Automation. Symposia Proceedings (Cat. No. 00CH37065), vol. 3. IEEE, 2000, pp. 2512–2517

    work page 2000

  9. [9]

    Outlier-robust estimation: Hardness, minimally tuned algorithms, and applications,

    P. Antonante, V . Tzoumas, H. Yang, and L. Carlone, “Outlier-robust estimation: Hardness, minimally tuned algorithms, and applications,” IEEE Transactions on Robotics, vol. 38, no. 1, pp. 281–301, 2021

    work page 2021

  10. [10]

    Clipper: A graph-theoretic framework for robust data association,

    P. C. Lusk, K. Fathian, and J. P. How, “Clipper: A graph-theoretic framework for robust data association,” in2021 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2021, pp. 13 828–13 834

    work page 2021

  11. [11]

    Netvlad: Cnn architecture for weakly supervised place recognition,

    R. Arandjelovic, P. Gronat, A. Torii, T. Pajdla, and J. Sivic, “Netvlad: Cnn architecture for weakly supervised place recognition,” inPro- ceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 5297–5307

    work page 2016

  12. [12]

    Learning combinatorial embedding networks for deep graph matching,

    R. Wang, J. Yan, and X. Yang, “Learning combinatorial embedding networks for deep graph matching,” inProceedings of the IEEE/CVF international conference on computer vision, 2019, pp. 3056–3065

    work page 2019

  13. [13]

    Mr-cographs: Communication-efficient multi- robot open-vocabulary mapping system via 3d scene graphs,

    Q. Gu, Z. Ye, J. Yu, J. Tang, T. Yi, Y . Dong, J. Wang, J. Cui, X. Chen, and Y . Wang, “Mr-cographs: Communication-efficient multi- robot open-vocabulary mapping system via 3d scene graphs,”IEEE Robotics and Automation Letters, 2025

    work page 2025

  14. [14]

    Tightly coupled slam with imprecise architectural plans,

    M. Shaheer, J. A. Millan-Romera, H. Bavle, M. Giberna, J. L. Sanchez-Lopez, J. Civera, and H. V oos, “Tightly coupled slam with imprecise architectural plans,”IEEE Robotics and Automation Letters, vol. 10, no. 8, pp. 8019–8026, 2025

    work page 2025

  15. [15]

    Sg-pgm: Partial graph matching network with semantic geometric fusion for 3d scene graph align- ment and its downstream tasks,

    Y . Xie, A. Pagani, and D. Stricker, “Sg-pgm: Partial graph matching network with semantic geometric fusion for 3d scene graph align- ment and its downstream tasks,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 28 401–28 411

    work page 2024

  16. [16]

    Building information modeling ( bim ) : Benefits , risks and challenges,

    S. Azhar, M. Hein, and B. Sketo, “Building information modeling ( bim ) : Benefits , risks and challenges,” 2008. [Online]. Available: https://api.semanticscholar.org/CorpusID:2418765

    work page 2008

  17. [17]

    Learning high-level semantic-relational concepts for slam,

    J. A. Millan-Romera, H. Bavle, M. Shaheer, M. R. Oswald, H. V oos, and J. L. Sanchez-Lopez, “Learning high-level semantic-relational concepts for slam,” in2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2024, pp. 9803–9810

    work page 2024

  18. [18]

    Design and use paradigms for gazebo, an open-source multi-robot simulator,

    N. Koenig and A. Howard, “Design and use paradigms for gazebo, an open-source multi-robot simulator,” in2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566), vol. 3, 2004, pp. 2149–2154 vol.3

    work page 2004

  19. [19]

    You only segment once: Towards real-time panoptic segmentation,

    J. Hu, L. Huang, T. Ren, S. Zhang, R. Ji, and L. Cao, “You only segment once: Towards real-time panoptic segmentation,” inProceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 17 819–17 829

    work page 2023