# S-Graphs+: Real-time Localization and Mapping leveraging Hierarchical Representations

arXiv: https://arxiv.org/abs/2212.11770 · HTML: https://mlsanity.com/paper/2212.11770/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 30 references that are in the map.
- First posted on arXiv 22 Dec 2022.

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

- [3D Scene Graph: A Structure for Unified Semantics, 3D Space, and Camera](https://mlsanity.com/paper/1910.02527/) (2019)
- [Learning 3D Semantic Scene Graphs from 3D Indoor Reconstructions](https://mlsanity.com/paper/2004.03967/) (2020)
- [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)
- [Voxblox: Incremental 3D Euclidean Signed Distance Fields for On-Board MAV Planning](https://mlsanity.com/paper/1611.03631/) (2016)
- [3D Dynamic Scene Graphs: Actionable Spatial Perception with Places, Objects, and Humans](https://mlsanity.com/paper/2002.06289/) (2020)
- [Kimera: from SLAM to Spatial Perception with 3D Dynamic Scene Graphs](https://mlsanity.com/paper/2101.06894/) (2021)
- [SuMa++: Efficient LiDAR-based Semantic SLAM](https://mlsanity.com/paper/2105.11320/) (2021)
- [Graph-to-3D: End-to-End Generation and Manipulation of 3D Scenes Using Scene Graphs](https://mlsanity.com/paper/2108.08841/) (2021)
- [3-D Scene Graph: A Sparse and Semantic Representation of Physical Environments for Intelligent Agents](https://mlsanity.com/paper/1908.04929/) (2019)
- [F-LOAM: Fast LiDAR Odometry And Mapping](https://mlsanity.com/paper/2107.00822/) (2021)
- [SegMap: Segment-based mapping and localization using data-driven descriptors](https://mlsanity.com/paper/1909.12837/) (2019)
- [From SLAM to Situational Awareness: Challenges and Survey](https://mlsanity.com/paper/2110.00273/) (2021)
- [Robust Odometry and Mapping for Multi-LiDAR Systems with Online Extrinsic Calibration](https://mlsanity.com/paper/2010.14294/) (2020)
- [Multi-Modal Lidar Dataset for Benchmarking General-Purpose Localization and Mapping Algorithms](https://mlsanity.com/paper/2203.03454/) (2022)
- [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)
- [Advanced Situational Graphs for Robot Navigation in Structured Indoor Environments](https://mlsanity.com/paper/2211.08754/) (2022)

## Built on by (most cited first, 28 of 28)

- [A Survey of Robotic Navigation and Manipulation with Physics Simulators in the Era of Embodied AI](https://mlsanity.com/paper/2505.01458/) (2025)
- [Language-EXtended Indoor SLAM (LEXIS): A Versatile System for Real-time Visual Scene Understanding](https://mlsanity.com/paper/2309.15065/) (2023)
- [Lost & Found: Tracking Changes from Egocentric Observations in 3D Dynamic Scene Graphs](https://mlsanity.com/paper/2411.19162/) (2024)
- [SG-Reg: Generalizable and Efficient Scene Graph Registration](https://mlsanity.com/paper/2504.14440/) (2025)
- [Language-Grounded Hierarchical Planning and Execution with Multi-Robot 3D Scene Graphs](https://mlsanity.com/paper/2506.07454/) (2025)
- [S-Graphs 2.0 -- A Hierarchical-Semantic Optimization and Loop Closure for SLAM](https://mlsanity.com/paper/2502.18044/) (2025)
- [Aion: Towards Hierarchical 4D Scene Graphs with Temporal Flow Dynamics](https://mlsanity.com/paper/2512.11903/) (2025)
- [Mono-hydra: Real-time 3D scene graph construction from monocular camera input with IMU](https://mlsanity.com/paper/2308.05515/) (2023)
- [Learning High-level Semantic-Relational Concepts for SLAM](https://mlsanity.com/paper/2310.00401/) (2023)
- [3D Scene Graphs: Open Challenges and Future Directions](https://mlsanity.com/paper/2606.19383/) (2026)
- [Graph-based Global Robot Localization Informing Situational Graphs with Architectural Graphs](https://mlsanity.com/paper/2303.02076/) (2023)
- [Generation of Uncertainty-Aware High-Level Spatial Concepts in Factorized 3D Scene Graphs via Graph Neural Networks](https://mlsanity.com/paper/2409.11972/) (2024)
- [A Landmark-Aware Visual Navigation Dataset](https://mlsanity.com/paper/2402.14281/) (2024)
- [vS-Graphs: Tightly Coupling Visual SLAM and 3D Scene Graphs Exploiting Hierarchical Scene Understanding](https://mlsanity.com/paper/2503.01783/) (2025)
- [Open-Vocabulary Indoor Object Grounding with 3D Hierarchical Scene Graph](https://mlsanity.com/paper/2507.12123/) (2025)
- [Integrating Prior Observations for Incremental 3D Scene Graph Prediction](https://mlsanity.com/paper/2509.11895/) (2025)
- [Think While You Map: Asynchronous Vision-Language Agents for Incremental 3D Scene Graphs](https://mlsanity.com/paper/2606.31471/) (2026)
- [Faster Optimization in S-Graphs Exploiting Hierarchy](https://mlsanity.com/paper/2308.11242/) (2023)
- [Feasibility of Indoor Frame-Wise Lidar Semantic Segmentation via Distillation from Visual Foundation Model](https://mlsanity.com/paper/2604.18831/) (2026)
- [LEXI-SG: Monocular 3D Scene Graph Mapping with Room-Guided Feed-Forward Reconstruction](https://mlsanity.com/paper/2605.13741/) (2026)
- [DSG: Dynamic 3D Scene Graph Construction for Embodied Agents in Changing Indoor Environments](https://mlsanity.com/paper/2609.00619/) (2026)
- [NavTopo: Leveraging Topological Maps For Autonomous Navigation Of a Mobile Robot](https://mlsanity.com/paper/2410.11492/) (2024)
- [OpenNavMap: Multi-Session Appearance-Based Topometric Mapping for Scalable Visual Navigation](https://mlsanity.com/paper/2601.12291/) (2026)
- [OpenSGA: Efficient 3D Scene Graph Alignment in the Open World](https://mlsanity.com/paper/2605.10484/) (2026)
- [Hydra++: Real-Time Hierarchical 3D Scene Graph Construction With Object-Level Shape Estimation](https://mlsanity.com/paper/2607.09455/) (2026)
- [Prior-SG: Task and Prior Driven Region Segmentation for Scene Graphs in Arbitrarily-Structured Environments](https://mlsanity.com/paper/2608.06170/) (2026)
- [Generation of High-Level Concepts in 3D Scene Graphs via Autoregressive Diffusion](https://mlsanity.com/paper/2608.28733/) (2026)
- [VideoReloc: Long-Term Indoor Video Relocalization against a Kilobyte-Scale Semantic Scene Graph](https://mlsanity.com/paper/2609.21804/) (2026)

## Abstract (arXiv)

In this paper, we present an evolved version of Situational Graphs, which jointly models in a single optimizable factor graph (1) a pose graph, as a set of robot keyframes comprising associated measurements and robot poses, and (2) a 3D scene graph, as a high-level representation of the environment that encodes its different geometric elements with semantic attributes and the relational information between them. Specifically, our S-Graphs+ is a novel four-layered factor graph that includes: (1) a keyframes layer with robot pose estimates, (2) a walls layer representing wall surfaces, (3) a rooms layer encompassing sets of wall planes, and (4) a floors layer gathering the rooms within a given floor level. The above graph is optimized in real-time to obtain a robust and accurate estimate of the robots pose and its map, simultaneously constructing and leveraging high-level information of the environment. To extract this high-level information, we present novel room and floor segmentation algorithms utilizing the mapped wall planes and free-space clusters. We tested S-Graphs+ on multiple datasets, including simulated and real data of indoor environments from varying construction sites, and on a real public dataset of several indoor office areas. On average over our datasets, S-Graphs+ outperforms the accuracy of the second-best method by a margin of 10.67%, while extending the robot situational awareness by a richer scene model. Moreover, we make the software available as a docker file.