This paper introduces 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. Our results demonstrate PanoRadar's robust performance across 12 buildings.

翻译：本文介绍了一种新型射频成像系统PanoRadar，该系统将射频分辨率提升至接近激光雷达的水平，同时具备应对光学信号难以适应环境条件的鲁棒性。我们获得的与激光雷达相当的3D成像结果，首次实现了多种射频视觉识别任务，包括表面法线估计、语义分割和目标检测。PanoRadar采用旋转式单芯片毫米波雷达，结合创新的信号处理与机器学习算法，生成高分辨率环境3D图像。本系统能精确估计机器人运动，通过密集合成天线阵列实现相干成像。同时，该系统利用高方位角分辨率，通过基于学习的方法提升俯仰角分辨率。此外，PanoRadar通过2D卷积处理3D学习任务，并针对射频信号独特特性带来的挑战提出了解决方案。实验结果表明，PanoRadar在12栋建筑场景中均表现出稳健的性能。