High Resolution Point Clouds from mmWave Radar
High Resolution Point Clouds from mmWave Radar
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DOI：
10.1109/icra48891.2023.10161429
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发表时间：
2022-06
期刊：
影响因子：
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通讯作者：
Akarsh Prabhakara;Tao Jin;Arnav Das;Gantavya Bhatt;Lilly Kumari;E. Soltanaghaei;J. Bilmes;Swarun Kumar;Anthony G. Rowe
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文献类型：
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作者：
Akarsh Prabhakara;Tao Jin;Arnav Das;Gantavya Bhatt;Lilly Kumari;E. Soltanaghaei;J. Bilmes;Swarun Kumar;Anthony G. Rowe
本文探讨了一种基于单芯片毫米波雷达数据的机器学习方法，用于生成高分辨率点云——这是机器人应用（如测绘、里程计和定位）的关键传感原语。与激光雷达和基于视觉的系统不同，毫米波雷达可以在恶劣的环境中工作，并能穿透烟雾、雾和灰尘等遮挡物。不幸的是，与激光雷达点云相比，目前的毫米波处理技术提供了较差的空间分辨率。RadarHD是一种端到端神经网络，利用低分辨率雷达输入构建类似激光雷达的点云。由于存在镜面反射和伪反射，增强雷达图像具有挑战性。雷达数据也不能很好地映射到传统的图像处理技术，因为信号的扩散模式类似于正弦。我们通过对RadarHD进行大量原始I/Q雷达数据的训练来克服这些挑战，这些数据与不同室内环境中的激光雷达点云相匹配。我们的实验表明，即使在训练期间未观察到的场景和存在浓烟遮挡的情况下，也能产生丰富的点云。此外，RadarHD的点云质量足够高，可以与现有的激光雷达里程计和测绘工作流程配合使用。
This paper explores a machine learning approach on data from a single-chip mmWave radar for generating high resolution point clouds – a key sensing primitive for robotic applications such as mapping, odometry and localization. Unlike lidar and vision-based systems, mmWave radar can operate in harsh environments and see through occlusions like smoke, fog, and dust. Unfortunately, current mmWave processing techniques offer poor spatial resolution compared to lidar point clouds. This paper presents RadarHD, an end-to-end neural network that constructs lidar-like point clouds from low resolution radar input. Enhancing radar images is challenging due to the presence of specular and spurious reflections. Radar data also doesn't map well to traditional image processing techniques due to the signal's sinc-like spreading pattern. We overcome these challenges by training RadarHD on a large volume of raw I/Q radar data paired with lidar point clouds across diverse indoor settings. Our experiments show the ability to generate rich point clouds even in scenes unobserved during training and in the presence of heavy smoke occlusion. Further, RadarHD's point clouds are high-quality enough to work with existing lidar odometry and mapping workflows.