4 papers
Millimeter Wave Radar: From Synthetic Aperture to Probabilistic Mapping
Jui-Te Huang, Ruoyang Xu, Michael Kaess
Robust probabilistic mapping is essential for autonomous robotic systems operating in challenging environments. While traditional sensors fail in adverse conditions such as smoke a…
UNRIO: Uncertainty-Aware Velocity Learning for Radar-Inertial Odometry
Jui-Te Huang, Tianshu Huang, Anthony Rowe +1
mmWave radars are robust to darkness and occlusions such as dust and smoke, and can directly constrain ego-velocity from a single frame via Doppler measurements, making them attrac…
Vision meets mmWave Radar: 3D Object Perception Benchmark for Autonomous Driving
Yizhou Wang, Jen-Hao Cheng, Jui-Te Huang +8
Sensor fusion is crucial for an accurate and robust perception system on autonomous vehicles. Most existing datasets and perception solutions focus on fusing cameras and LiDAR. How…
Multi-Radar Inertial Odometry for 3D State Estimation using mmWave Imaging Radar
Jui-Te Huang, Ruoyang Xu, Akshay Hinduja +1
State estimation is a crucial component for the successful implementation of robotic systems, relying on sensors such as cameras, LiDAR, and IMUs. However, in real-world scenarios,…