Yongwei Zheng
Papers
1
Total Citations
32
H-Index
1
About
Yongwei Zheng is a leading researcher in 3D sensing and autonomous robotics, with a primary focus on LiDAR perception and simultaneous localization and mapping (SLAM). His key research areas include LiDAR super-resolution, point cloud processing, and structure-guided deep learning for robotic perception. Zheng’s most notable contribution is the development of SGSR-Net (Structure Semantics Guided LiDAR Super-Resolution Network), a pioneering framework that enhances the resolution of multi-beam LiDAR point clouds for indoor SLAM applications. This work, published in 2023 and already garnering 32 citations, addresses a critical limitation in autonomous navigation: the trade-off between sensor cost and point cloud density. By leveraging structural and semantic cues, SGSR-Net enables lower-resolution LiDAR sensors to achieve the high-fidelity surface sampling typically requiring expensive 128-beam devices like the Ouster OS0-128. This breakthrough has significant implications for making high-performance robotic perception more accessible. Zheng’s research is widely recognized for bridging the gap between sensor hardware constraints and algorithmic innovation, with his work cited by peers advancing LiDAR-based mapping and autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1