This work introduces a learning-based approach to accurately estimate the height of radar points, enabling refined radar data for downstream perception tasks such as object detection and depth estimation.
This paper presents a novel method for estimating 2D ego-motion, including yaw rate, using only mmWave radar sensors without the need for additional sensors like IMUs or LiDARs.
Proposing a novel approach using diffusion models to generate dense and accurate LiDAR-like point clouds from sparse radar data for MAV autonomous navigation.