4D毫米波雷达稀疏点云下的连续帧隐空间法线估计

Consecutive-frame latent space normal estimation under sparse point clouds for 4D millimeter-wave radar

针对毫米波连续调频雷达在室外运动场景下点云数据稀疏和空间配准精度低的问题,本文提出了一种轻量级空间配准方法。该方法适用于室外运动场景下毫米波雷达连续多帧之间的点云处理,能在没有位姿传感器辅助的情况下,通过时空图神经网络准确估计相邻多帧点云的隐空间法线,并将每帧雷达点云数据转换到统一的观测坐标系中,从而实现四维点云的多帧融合与场景配准。实验结果表明,该方法不仅能准确评估四维点云的空间姿态,还能有效校正和融合每帧点云的坐标,在运动及震动过程中实现点云坐标的精准配准。此外,该算法还能显著提高点云成像的密度,增强图像的精度和可读性,同时适用于静态和动态目标的成像,为毫米波雷达在室外运动场景的应用提供了有力支持。

Aiming at the sparsity of point cloud data and the low accuracy of spatial alignment exhibited by millimeter-wave frequency-modulated continuous-wave(FMCW)radar in outdoor motion scenarios,a lightweight model for spatial alignment was proposed.This method was specifically tailored for point cloud processing across consecutive multi-frames in outdoor motion scenes captured by millimeter-wave radar.Leveraging spatio-temporal graph neural networks(ST-GNNs),it accurately estimated the hidden spatial normals of adjacent multi-frame point clouds,eliminating the need for position sensors.By transforming radar point cloud data from each frame into a unified observation coordinate system,the method facilitated multi-frame fusion of 4D point clouds and ensured precise scene alignment.Experimental results demonstrated that the proposed approach not only accurately assessed the spatial attitude of 4D point clouds but also effectively corrected and fused the coordinates of each point cloud frame.This enabled precise coordinate alignment during motion and vibration.Furthermore,the algorithm significantly enhanced point cloud imaging density,improved image accuracy and readability,and was capable of imaging both static and dynamic targets.It provided robust support for the application of millimeter-wave radar in outdoor motion scenes.