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

arXiv: https://arxiv.org/abs/2507.00937 · HTML: https://mlsanity.com/paper/2507.00937/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 52 references that are in the map.
- First posted on arXiv 1 Jul 2025.

## Builds on (most cited first, 17 of 17)

- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [The Marathon 2: A Navigation System](https://mlsanity.com/paper/2003.00368/) (2020)
- [See Through Smoke: Robust Indoor Mapping with Low-cost mmWave Radar](https://mlsanity.com/paper/1911.00398/) (2019)
- [RadarGNN: Transformation Invariant Graph Neural Network for Radar-based Perception](https://mlsanity.com/paper/2304.06547/) (2023)
- [High Resolution Point Clouds from mmWave Radar](https://mlsanity.com/paper/2206.09273/) (2022)
- [DREAM-PCD: Deep Reconstruction and Enhancement of mmWave Radar Pointcloud](https://mlsanity.com/paper/2309.15374/) (2023)
- [From the Desks of ROS Maintainers: A Survey of Modern & Capable Mobile Robotics Algorithms in the Robot Operating System 2](https://mlsanity.com/paper/2307.15236/) (2023)
- [Enabling Visual Recognition at Radio Frequency](https://mlsanity.com/paper/2405.19516/) (2024)
- [Lidar-level localization with radar? The CFEAR approach to accurate, fast and robust large-scale radar odometry in diverse environments](https://mlsanity.com/paper/2211.02445/) (2022)
- [Radarize: Enhancing Radar SLAM with Generalizable Doppler-Based Odometry](https://mlsanity.com/paper/2311.11260/) (2023)
- [RadCloud: Real-Time High-Resolution Point Cloud Generation Using Low-Cost Radars for Aerial and Ground Vehicles](https://mlsanity.com/paper/2403.05964/) (2024)
- [Are Doppler Velocity Measurements Useful for Spinning Radar Odometry?](https://mlsanity.com/paper/2404.01537/) (2024)
- [Relaxing Integrity Requirements for Attack-Resilient Cyber-Physical Systems](https://mlsanity.com/paper/1707.02950/) (2017)
- [Doppler-aware Odometry from FMCW Scanning Radar](https://mlsanity.com/paper/2308.10597/) (2023)
- [Automotive-Radar-Based 50-cm Urban Positioning](https://mlsanity.com/paper/2005.00704/) (2020)
- [Optimal Myopic Attacks on Nonlinear Estimation](https://mlsanity.com/paper/2204.06996/) (2022)

## Abstract (arXiv)

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.