RadCloud: Real-Time High-Resolution Point Cloud Generation Using Low-Cost mmWave Radars for Aerial and Ground Vehicles
Year: 2024
Type: conference-paper
Abstract: 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.
Authors: David Hunt, Shaocheng Luo, Amir Khazraei, Xiao Zhang, Spencer Hallyburton, Tingjun Chen, Miroslav Pajic
Institution: Duke University
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Awards: G1257962346, G1815185883, CAREER: Foundations for Secure Control of Cyber-Physical Systems, Collaborative Research: CNS Core: Medium: Softwarizing Millimeter-wave Radio Access Networks (RANs) at the Edge, AI Institute for Edge Computing Leveraging Next Generation Networks (Athena), Collaborative Research: SII-NRDZ:Spectrum Sharing via Consumption Models and Telemetry - Prototyping and Field Testing in an Urban FCC Innovation Zone
OpenAlex ID: W4405014833