RaGNNarok: A Light-Weight Graph Neural Network for Enhancing Radar Point Clouds on Unmanned Ground Vehicles

TL;DR

RaGNNarok is a lightweight graph neural network framework that significantly enhances radar point cloud data for indoor mobile robots, enabling accurate localization and navigation on low-cost hardware with minimal inference time.

Contribution

This work introduces RaGNNarok, a novel GNN-based method that improves radar point cloud quality for localization and SLAM, optimized for real-time performance on resource-constrained devices.

Findings

Achieves 7.3 ms inference time on Raspberry Pi 5

Enhances radar point clouds for better localization and SLAM

Demonstrates robustness across diverse indoor environments

Abstract

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.