Current lidar and camera-based solutions for low-cost indoor mobile robots 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 only 7.3 ms on the low-cost Raspberry Pi 5, RaGNNarok runs even on such resource-constrained devices, without 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.

In this work, we present RadCloud, a novel real-time framework for directly obtaining higher-resolution lidar-like 2D point clouds from low-resolution radar frames on resource-constrained platforms commonly used in unmanned aerial and ground vehicles (UAVs and UGVs, respectively); such point clouds can then be used for accurate environmental mapping, navigating unknown environments, and other robotics tasks. While high-resolution sensing using radar data has been previously reported, existing methods cannot be used on most UAVs, which have limited computational power and energy; thus, existing demonstrations focus on offline radar processing. RadCloud overcomes these challenges by using a radar configuration with 1/4th of the range resolution and employing a deep learning model with 2.25× fewer parameters. Additionally, RadCloud utilizes a novel chirp-based approach that makes obtained point clouds resilient to rapid movements (e.g., aggressive turns or spins) that commonly occur during UAV flights. In real-world experiments, we demonstrate the accuracy and applicability of RadCloud on commercially available UAVs and UGVs, with off-the-shelf radar platforms on-board.

We demonstrate RadCloud, a real-time framework for obtaining high-resolution lidar-like 2D point clouds from low-resolution millimeter-wave (mmWave) radar data on resource-constrained platforms commonly found on unmanned aerial and ground vehicles (UAVs and UGVs). Such point clouds can then be used for mapping key features of the environment, route planning and navigation, and other robotics tasks. Rad-Cloud is specifically optimized for UAVs and UGVs by using a radar configuration with 1/4th the range resolution, using a model with 2.25× fewer parameters, and reducing total sensing time by a factor of 250×. The real-time ROS framework will be demonstrated on a UGV and UAV equipped with CPU-only compute platforms in diverse environments.