サインイン
インサイト - Radar Perception
Enhancing Radar Perception through Multi-Task Learning: Refining Data for Sensor Fusion Applications
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.
Estimating 2D Ego-Motion and Yaw using Only mmWave Radars via Two-Way Weighted ICP
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.
Enhancing Radar Perception for MAV Navigation with Diffusion Models
Proposing a novel approach using diffusion models to generate dense and accurate LiDAR-like point clouds from sparse radar data for MAV autonomous navigation.
