# Situational Graphs for Robot Navigation in Structured Indoor Environments

arXiv: https://arxiv.org/abs/2202.12197 · HTML: https://mlsanity.com/paper/2202.12197/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 37 references that are in the map.
- First posted on arXiv 24 Feb 2022.

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

- [ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras](https://mlsanity.com/paper/1610.06475/) (2016)
- [Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age](https://mlsanity.com/paper/1606.05830/) (2016)
- [3D Scene Graph: A Structure for Unified Semantics, 3D Space, and Camera](https://mlsanity.com/paper/1910.02527/) (2019)
- [LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping](https://mlsanity.com/paper/2007.00258/) (2020)
- [Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping](https://mlsanity.com/paper/1910.02490/) (2019)
- [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)
- [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)
- [Volumetric Instance-Aware Semantic Mapping and 3D Object Discovery](https://mlsanity.com/paper/1903.00268/) (2019)
- [LVI-SAM: Tightly-coupled Lidar-Visual-Inertial Odometry via Smoothing and Mapping](https://mlsanity.com/paper/2104.10831/) (2021)
- [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)
- [Tightly Coupled 3D Lidar Inertial Odometry and Mapping](https://mlsanity.com/paper/1904.06993/) (2019)
- [LIMO: Lidar-Monocular Visual Odometry](https://mlsanity.com/paper/1807.07524/) (2018)
- [Probabilistic Data Association via Mixture Models for Robust Semantic SLAM](https://mlsanity.com/paper/1909.11213/) (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)
- [SA-LOAM: Semantic-aided LiDAR SLAM with Loop Closure](https://mlsanity.com/paper/2106.11516/) (2021)
- [LIRO: Tightly Coupled Lidar-Inertia-Ranging Odometry](https://mlsanity.com/paper/2010.13072/) (2020)

## Built on by (most cited first, 21 of 21)

- [Hierarchical Open-Vocabulary 3D Scene Graphs for Language-Grounded Robot Navigation](https://mlsanity.com/paper/2403.17846/) (2024)
- [Visual SLAM: What are the Current Trends and What to Expect?](https://mlsanity.com/paper/2210.10491/) (2022)
- [SG-Reg: Generalizable and Efficient Scene Graph Registration](https://mlsanity.com/paper/2504.14440/) (2025)
- [QueSTMaps: Queryable Semantic Topological Maps for 3D Scene Understanding](https://mlsanity.com/paper/2404.06442/) (2024)
- [S-Graphs 2.0 -- A Hierarchical-Semantic Optimization and Loop Closure for SLAM](https://mlsanity.com/paper/2502.18044/) (2025)
- [Learning High-level Semantic-Relational Concepts for SLAM](https://mlsanity.com/paper/2310.00401/) (2023)
- [Belief Scene Graphs: Expanding Partial Scenes with Objects through Computation of Expectation](https://mlsanity.com/paper/2402.03840/) (2024)
- [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)
- [Estimating Commonsense Scene Composition on Belief Scene Graphs](https://mlsanity.com/paper/2505.02405/) (2025)
- [Advanced Situational Graphs for Robot Navigation in Structured Indoor Environments](https://mlsanity.com/paper/2211.08754/) (2022)
- [vS-Graphs: Tightly Coupling Visual SLAM and 3D Scene Graphs Exploiting Hierarchical Scene Understanding](https://mlsanity.com/paper/2503.01783/) (2025)
- [Faster Optimization in S-Graphs Exploiting Hierarchy](https://mlsanity.com/paper/2308.11242/) (2023)
- [VIZOR: Viewpoint-Invariant Zero-Shot Scene Graph Generation for 3D Scene Reasoning](https://mlsanity.com/paper/2602.00637/) (2026)
- [Guessing human intentions to avoid dangerous situations in caregiving robots](https://mlsanity.com/paper/2403.16291/) (2024)
- [OpenNavMap: Multi-Session Appearance-Based Topometric Mapping for Scalable Visual Navigation](https://mlsanity.com/paper/2601.12291/) (2026)
- [PlanaReLoc: Camera Relocalization in 3D Planar Primitives via Region-Based Structure Matching](https://mlsanity.com/paper/2603.20818/) (2026)
- [Hydra++: Real-Time Hierarchical 3D Scene Graph Construction With Object-Level Shape Estimation](https://mlsanity.com/paper/2607.09455/) (2026)
- [What VGGT Knows About Overlap: Probing Geometric Foundation Models for Co-Visibility](https://mlsanity.com/paper/2607.09503/) (2026)
- [Prior-SG: Task and Prior Driven Region Segmentation for Scene Graphs in Arbitrarily-Structured Environments](https://mlsanity.com/paper/2608.06170/) (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)

Mobile robots should be aware of their situation, comprising the deep understanding of their surrounding environment along with the estimation of its own state, to successfully make intelligent decisions and execute tasks autonomously in real environments. 3D scene graphs are an emerging field of research that propose to represent the environment in a joint model comprising geometric, semantic and relational/topological dimensions. Although 3D scene graphs have already been combined with SLAM techniques to provide robots with situational understanding, further research is still required to effectively deploy them on-board mobile robots. To this end, we present in this paper a novel, real-time, online built Situational Graph (S-Graph), which combines in a single optimizable graph, the representation of the environment with the aforementioned three dimensions, together with the robot pose. Our method utilizes odometry readings and planar surfaces extracted from 3D LiDAR scans, to construct and optimize in real-time a three layered S-Graph that includes (1) a robot tracking layer where the robot poses are registered, (2) a metric-semantic layer with features such as planar walls and (3) our novel topological layer constraining the planar walls using higher-level features such as corridors and rooms. Our proposal does not only demonstrate state-of-the-art results for pose estimation of the robot, but also contributes with a metric-semantic-topological model of the environment