
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.


coremsg

enhancing-radar-perception-through-multi-task-learning-refining-data-for-sensor-fusion-applications


Enhancing Radar Perception through Multi-Task Learning: Refining Data for Sensor Fusion Applications


title_rewrite


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.


estimating-2d-ego-motion-and-yaw-using-only-mmwave-radars-via-two-way-weighted-icp


Estimating 2D Ego-Motion and Yaw using Only mmWave Radars via Two-Way Weighted ICP



Proposing a novel approach using diffusion models to generate dense and accurate LiDAR-like point clouds from sparse radar data for MAV autonomous navigation.


enhancing-radar-perception-for-mav-navigation-with-diffusion-models


Enhancing Radar Perception for MAV Navigation with Diffusion Models
