轻量图网络提升雷达点云，让低成本机器人在复杂环境稳定导航
RaGNNarok: A Light-Weight Graph Neural Network for Enhancing Radar Point Clouds on Unmanned Ground Vehicles
- 基于图神经网络构建轻量框架，实时处理雷达点云
- 在树莓派5上仅需7.3毫秒推理时间，无额外算力需求
- 适用于低成成本室内机器人，支持定位、建图与自主导航
低成本室内移动机器人因家庭与商业空间自动化普及而日益流行。现有基于激光雷达和摄像头的方案存在视觉遮挡下性能差、数据处理计算开销大、激光雷达成本高等问题。毫米波雷达传感器则提供低成本、轻量化且不受可见性影响的测距能力。然而，现有雷达定位面临点云稀疏、噪声和误检等问题。为此，本文提出RaGNNarok，一种实时、轻量、可泛化的图神经网络（GNN）框架，用于增强雷达点云，即使在复杂动态环境中亦表现良好。该模型在低成本树莓派5上仅需7.3毫秒推理时间，无需额外计算资源。我们在三种不同环境下评估其在定位、SLAM和自主导航任务中的性能，结果表明其具备强可靠性与泛化能力，是低成本室内移动机器人的可靠解决方案。
原文摘要 · Abstract (English)
Low-cost indoor mobile robots have gained popularity with the increasing adoption of automation in homes and commercial spaces. However, existing lidar and camera-based solutions have limitations such as poor performance in visually obscured environments, high computational overhead for data processing, and high costs for lidars. In contrast, mmWave radar sensors offer a cost-effective and lightweight alternative, providing accurate ranging regardless of visibility. However, existing radar-based localization suffers from sparse point cloud generation, noise, and false detections. Thus, in this work, we introduce RaGNNarok, a real-time, lightweight, and generalizable graph neural network (GNN)-based framework to enhance radar point clouds, even in complex and dynamic environments. With an inference time of just 7.3 ms on the low-cost Raspberry Pi 5, RaGNNarok runs efficiently even on such resource-constrained devices, requiring no additional computational resources. We evaluate its performance across key tasks, including localization, SLAM, and autonomous navigation, in three different environments. Our results demonstrate strong reliability and generalizability, making RaGNNarok a robust solution for low-cost indoor mobile robots.
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