電波による視覚認識の実現
Enabling Visual Recognition at Radio Frequency
回転式ミリ波レーダーと信号処理・機械学習を組み合わせ、LiDARに匹敵する高解像度3Dイメージングを実現し、電波領域での表面法線推定・意味セグメンテーション・物体検出などの視覚認識タスクを可能にした。
著者: Haowen Lai, Gaoxiang Luo, Yifei Liu, Mingmin Zhao
分類: eess.SP, cs.CV, cs.LG, cs.RO
原文アブストラクト
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.
