  Complementary to prevalent LiDAR and camera systems, millimeter-wave (mmWave)
radar is robust to adverse weather conditions like fog, rainstorms, and
blizzards but offers sparse point clouds. Current techniques enhance the point
cloud by the supervision of LiDAR's data. However, high-performance LiDAR is
notably expensive and is not commonly available on vehicles. This paper
presents mmEMP, a supervised learning approach that enhances radar point clouds
using a low-cost camera and an inertial measurement unit (IMU), enabling
crowdsourcing training data from commercial vehicles. Bringing the
visual-inertial (VI) supervision is challenging due to the spatial agnostic of
dynamic objects. Moreover, spurious radar points from the curse of RF multipath
make robots misunderstand the scene. mmEMP first devises a dynamic 3D
reconstruction algorithm that restores the 3D positions of dynamic features.
Then, we design a neural network that densifies radar data and eliminates
spurious radar points. We build a new dataset in the real world. Extensive
experiments show that mmEMP achieves competitive performance compared with the
SOTA approach training by LiDAR's data. In addition, we use the enhanced point
cloud to perform object detection, localization, and mapping to demonstrate
mmEMP's effectiveness.
