用地板特征图与图神经网络实现厘米级精准机器人定位
Graph-based Robot Localization Using a Graph Neural Network with a Floor Camera and a Feature Rich Industrial Floor
- 构建地板特征图，通过图卷积网络进行端到端定位
- 定位误差仅0.64厘米，显著优于传统方法
- 无需复杂滤波即可实时解决“被劫持”问题，适合工业场景
精准定位是机器人导航中的核心挑战。传统方法如激光雷达或二维码系统在复杂环境中存在可扩展性和适应性不足的问题。本文提出一种基于图表示和图卷积网络（GCNs）的创新定位框架，利用地板特征构建图结构，实现更准确（误差0.64cm）且高效的定位。该方法无需复杂的滤波过程，可在每帧中有效解决“被劫持”机器人问题，为多样化环境下的机器人导航提供了新可能。
原文摘要 · Abstract (English)
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.
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