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

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 constraints, particularly in complex environments. In this work, we propose an innovative localization framework that harnesses flooring characteristics 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. 64 cm 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.