# Graph-based Robot Localization Using a Graph Neural Network with a Floor Camera and a Feature Rich Industrial Floor

arXiv: https://arxiv.org/abs/2508.06177 · HTML: https://mlsanity.com/paper/2508.06177/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 15 references that are in the map.
- First posted on arXiv 8 Aug 2025.

## Builds on (most cited first, 7 of 7)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Curriculum Learning: A Survey](https://mlsanity.com/paper/2101.10382/) (2021)
- [SimGNN: A Neural Network Approach to Fast Graph Similarity Computation](https://mlsanity.com/paper/1808.05689/) (2018)
- [High-Precision Localization Using Ground Texture](https://mlsanity.com/paper/1710.10687/) (2017)
- [Features for Ground Texture Based Localization -- A Survey](https://mlsanity.com/paper/2002.11948/) (2020)
- [Robot Localization Using a Learned Keypoint Detector and Descriptor with a Floor Camera and a Feature Rich Industrial Floor](https://mlsanity.com/paper/2504.03249/) (2025)

## Abstract (arXiv)

Accurate localization represents a fundamental challenge in robotic navigation. Traditional methodologies, such as Lidar or QR-code based systems, suffer from inherent scalability and adaptability con straints, particularly in complex environments. In this work, we propose an innovative localization framework that harnesses flooring characteris tics by employing graph-based representations and Graph Convolutional Networks (GCNs). Our method uses graphs to represent floor features, which helps localize the robot more accurately (0.64cm error) and more efficiently than comparing individual image features. Additionally, this approach successfully addresses the kidnapped robot problem in every frame without requiring complex filtering processes. These advancements open up new possibilities for robotic navigation in diverse environments.