# Learning High-level Semantic-Relational Concepts for SLAM

arXiv: https://arxiv.org/abs/2310.00401 · HTML: https://mlsanity.com/paper/2310.00401/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 27 references that are in the map.
- First posted on arXiv 30 Sep 2023.

## Builds on (most cited first, 20 of 20)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Visual Language Maps for Robot Navigation](https://mlsanity.com/paper/2210.05714/) (2022)
- [ConceptGraphs: Open-Vocabulary 3D Scene Graphs for Perception and Planning](https://mlsanity.com/paper/2309.16650/) (2023)
- [3D Scene Graph: A Structure for Unified Semantics, 3D Space, and Camera](https://mlsanity.com/paper/1910.02527/) (2019)
- [Hydra: A Real-time Spatial Perception System for 3D Scene Graph Construction and Optimization](https://mlsanity.com/paper/2201.13360/) (2022)
- [SceneGraphFusion: Incremental 3D Scene Graph Prediction from RGB-D Sequences](https://mlsanity.com/paper/2103.14898/) (2021)
- [3D Dynamic Scene Graphs: Actionable Spatial Perception with Places, Objects, and Humans](https://mlsanity.com/paper/2002.06289/) (2020)
- [3-D Scene Graph: A Sparse and Semantic Representation of Physical Environments for Intelligent Agents](https://mlsanity.com/paper/1908.04929/) (2019)
- [Foundations of Spatial Perception for Robotics: Hierarchical Representations and Real-time Systems](https://mlsanity.com/paper/2305.07154/) (2023)
- [Collaborative Dynamic 3D Scene Graphs for Automated Driving](https://mlsanity.com/paper/2309.06635/) (2023)
- [S-Graphs+: Real-time Localization and Mapping leveraging Hierarchical Representations](https://mlsanity.com/paper/2212.11770/) (2022)
- [Situational Graphs for Robot Navigation in Structured Indoor Environments](https://mlsanity.com/paper/2202.12197/) (2022)
- [From SLAM to Situational Awareness: Challenges and Survey](https://mlsanity.com/paper/2110.00273/) (2021)
- [Neural Trees for Learning on Graphs](https://mlsanity.com/paper/2105.07264/) (2021)
- [Robust Structure Identification and Room Segmentation of Cluttered Indoor Environments from Occupancy Grid Maps](https://mlsanity.com/paper/2203.03519/) (2022)
- [Sparse 3D Topological Graphs for Micro-Aerial Vehicle Planning](https://mlsanity.com/paper/1803.04345/) (2018)
- [Automatic reconstruction of fully volumetric 3D building models from point clouds](https://mlsanity.com/paper/1907.00631/) (2019)
- [A method to segment maps from different modalities using free space layout -- MAORIS : MAp Of RIpples Segmentation](https://mlsanity.com/paper/1709.09899/) (2017)
- [S-Nav: Semantic-Geometric Planning for Mobile Robots](https://mlsanity.com/paper/2307.01613/) (2023)

## Built on by (most cited first, 4 of 4)

- [S-Graphs 2.0 -- A Hierarchical-Semantic Optimization and Loop Closure for SLAM](https://mlsanity.com/paper/2502.18044/) (2025)
- [Generation of Uncertainty-Aware High-Level Spatial Concepts in Factorized 3D Scene Graphs via Graph Neural Networks](https://mlsanity.com/paper/2409.11972/) (2024)
- [Generation of High-Level Concepts in 3D Scene Graphs via Autoregressive Diffusion](https://mlsanity.com/paper/2608.28733/) (2026)
- [A Review Of Robotic World Models For Dynamic Environments Based On Factor And Scene Graphs](https://mlsanity.com/paper/2610.08800/) (2026)

## Abstract (arXiv)

Recent works on SLAM extend their pose graphs with higher-level semantic concepts like Rooms exploiting relationships between them, to provide, not only a richer representation of the situation/environment but also to improve the accuracy of its estimation. Concretely, our previous work, Situational Graphs (S-Graphs+), a pioneer in jointly leveraging semantic relationships in the factor optimization process, relies on semantic entities such as Planes and Rooms, whose relationship is mathematically defined. Nevertheless, there is no unique approach to finding all the hidden patterns in lower-level factor-graphs that correspond to high-level concepts of different natures. It is currently tackled with ad-hoc algorithms, which limits its graph expressiveness. To overcome this limitation, in this work, we propose an algorithm based on Graph Neural Networks for learning high-level semantic-relational concepts that can be inferred from the low-level factor graph. Given a set of mapped Planes our algorithm is capable of inferring Room entities relating to the Planes. Additionally, to demonstrate the versatility of our method, our algorithm can infer an additional semantic-relational concept, i.e. Wall, and its relationship with its Planes. We validate our method in both simulated and real datasets demonstrating improved performance over two baseline approaches. Furthermore, we integrate our method into the S-Graphs+ algorithm providing improved pose and map accuracy compared to the baseline while further enhancing the scene representation.