  This paper explores a machine learning approach for generating high
resolution point clouds from a single-chip mmWave radar. 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.
