Year: 2024
Type: conference-paper
Abstract: This demo presents PanoRadar, a novel RF imaging system that brings RF resolution close to that of LiDAR, while providing resilience against conditions challenging for optical signals. Our LiDAR-comparable 3D imaging results enable, for the first time, a variety of visual recognition tasks at radio frequency, including surface normal estimation, semantic segmentation, and object detection. PanoRadar utilizes a rotating single-chip mmWave radar, along with a combination of novel signal processing and machine learning algorithms, to create high-resolution 3D images of the surroundings. Our system accurately estimates robot motion, allowing for coherent imaging through a dense grid of synthetic antennas. It also exploits the high azimuth resolution to enhance elevation resolution using learning-based methods. Furthermore, PanoRadar tackles 3D learning via 2D convolutions and addresses challenges due to the unique characteristics of RF signals. This demonstration illustrates the ability of high-resolution RF imaging of PanoRadar. We present the signal processing results to show the high range and azimuth resolution of the system, and the machine learning results for elevation resolution enhancement. Code, datasets, and demo videos are available on our website.
Authors: Haowen Lai, Yifei Liu, Gaoxiang Luo, Mingmin Zhao
Institution: University of Pennsylvania
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4405014252