Graph-based Robot Localization Using a Graph Neural Network with a Floor Camera and a Feature Rich Industrial Floor
This addresses the problem of accurate and scalable localization for robots in complex environments, representing an incremental improvement over traditional methods like Lidar or QR-code systems.
The paper tackles robot localization by using graph-based representations and Graph Convolutional Networks (GCNs) to leverage floor features, achieving a localization error of 0.64cm and solving the kidnapped robot problem per frame without complex filtering.
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.