Papers
8
Total Citations
214
H-Index
6
About
Yuwei Cheng is an emerging researcher specializing in millimeter-wave (mmWave) radar perception, autonomous robotics, and mobile robot localization. His work addresses a critical challenge in autonomous systems: enabling robust perception and navigation under adverse environmental conditions where traditional optical sensors fail. Cheng's most influential contribution, "A Novel Radar Point Cloud Generation Method for Robot Environment Perception" (2022, 96 citations), established new approaches to extracting high-quality spatial data from mmWave radar hardware — a foundational advance for all-weather autonomous driving and robotics. He has further developed radar-inertial odometry through DRIO (31 citations), tackling the difficult problem of accurate localization in dynamic environments, and pioneered radar-based relocalization for visually degraded settings (16 citations), pushing the boundaries of SLAM systems beyond LiDAR and camera dependence. Beyond perception, Cheng demonstrates impressive breadth with SMURF (38 citations), a fully autonomous water surface cleaning robot featuring novel coverage path planning — addressing real-world environmental challenges. His recent work on diffusion-based radar point cloud super-resolution and multi-sensor fusion odometry reflects a sustained commitment to advancing radar's practical utility. With over 210 cumulative citations, Cheng is rapidly establishing himself as a leading voice in radar-centric autonomous systems research.
Research Focus
Key Achievements
Top Papers
- 1A Novel Radar Point Cloud Generation Method for Robot Environment Perception96 citations · 2022
- 2
- 3DRIO: Robust Radar-Inertial Odometry in Dynamic Environments31 citations · 2023
- 4
- 5Diffusion-Based Point Cloud Super-Resolution for mmWave Radar Data14 citations · 2024
- 6MS-VRO: A Multistage Visual-Millimeter Wave Radar Fusion Odometry10 citations · 2024
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