# Graph-based Global Robot Localization Informing Situational Graphs with Architectural Graphs

arXiv: https://arxiv.org/abs/2303.02076 · HTML: https://mlsanity.com/paper/2303.02076/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 49 references that are in the map.
- First posted on arXiv 3 Mar 2023.

## Builds on (most cited first, 16 of 16)

- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [3D Scene Graph: A Structure for Unified Semantics, 3D Space, and Camera](https://mlsanity.com/paper/1910.02527/) (2019)
- [Modelling Uncertainty in Deep Learning for Camera Relocalization](https://mlsanity.com/paper/1509.05909/) (2015)
- [3-D Scene Graph: A Sparse and Semantic Representation of Physical Environments for Intelligent Agents](https://mlsanity.com/paper/1908.04929/) (2019)
- [SegMap: Segment-based mapping and localization using data-driven descriptors](https://mlsanity.com/paper/1909.12837/) (2019)
- [CLIPPER: A Graph-Theoretic Framework for Robust Data Association](https://mlsanity.com/paper/2011.10202/) (2020)
- [S-Graphs+: Real-time Localization and Mapping leveraging Hierarchical Representations](https://mlsanity.com/paper/2212.11770/) (2022)
- [Robot Localization in Floor Plans Using a Room Layout Edge Extraction Network](https://mlsanity.com/paper/1903.01804/) (2019)
- [Outlier-Robust Estimation: Hardness, Minimally Tuned Algorithms, and Applications](https://mlsanity.com/paper/2007.15109/) (2020)
- [Satellite Image-based Localization via Learned Embeddings](https://mlsanity.com/paper/1704.01133/) (2017)
- [Situational Graphs for Robot Navigation in Structured Indoor Environments](https://mlsanity.com/paper/2202.12197/) (2022)
- [Any Way You Look At It: Semantic Crossview Localization and Mapping with LiDAR](https://mlsanity.com/paper/2203.08925/) (2022)
- [Semantic localization on BIM-generated maps using a 3D LiDAR sensor](https://mlsanity.com/paper/2205.00816/) (2022)
- [Precise Robot Localization in Architectural 3D Plans](https://mlsanity.com/paper/2006.05137/) (2020)
- [FP-Loc: Lightweight and Drift-free Floor Plan-assisted LiDAR Localization](https://mlsanity.com/paper/2203.00292/) (2022)
- [Robot Localization using Situational Graphs and Building Architectural Plans](https://mlsanity.com/paper/2209.11575/) (2022)

## Built on by (most cited first, 3 of 3)

- [VeTraSS: Vehicle Trajectory Similarity Search Through Graph Modeling and Representation Learning](https://mlsanity.com/paper/2404.08021/) (2024)
- [Guessing human intentions to avoid dangerous situations in caregiving robots](https://mlsanity.com/paper/2403.16291/) (2024)
- [COMPASS: COmpact Multi-channel Prior-map And Scene Signature for Floor-Plan-Based Visual Localization](https://mlsanity.com/paper/2604.25388/) (2026)

## Abstract (arXiv)

In this paper, we propose a solution for legged robot localization using architectural plans. Our specific contributions towards this goal are several. Firstly, we develop a method for converting the plan of a building into what we denote as an architectural graph (A-Graph). When the robot starts moving in an environment, we assume it has no knowledge about it, and it estimates an online situational graph representation (S-Graph) of its surroundings. We develop a novel graph-to-graph matching method, in order to relate the S-Graph estimated online from the robot sensors and the A-Graph extracted from the building plans. Note the challenge in this, as the S-Graph may show a partial view of the full A-Graph, their nodes are heterogeneous and their reference frames are different. After the matching, both graphs are aligned and merged, resulting in what we denote as an informed Situational Graph (iS-Graph), with which we achieve global robot localization and exploitation of prior knowledge from the building plans. Our experiments show that our pipeline shows a higher robustness and a significantly lower pose error than several LiDAR localization baselines.