Yukang Su
Papers
1
Total Citations
3
H-Index
1
About
Yukang Su is a robotics researcher whose work centers on advancing 3D LiDAR-based perception and state estimation for autonomous systems. His primary research areas include LiDAR odometry, simultaneous localization and mapping (SLAM), and point cloud feature extraction. Su’s most notable contribution is the development of CDP-LOAM, a novel LiDAR odometry and mapping method that introduces a seven-dimensional clustering-directed feature point approach. This method significantly improves robot attitude estimation by intelligently selecting and matching feature points from clustered point cloud data, enhancing both accuracy and robustness in complex environments. While his most-cited paper, published in 2024, has garnered 3 citations in its early stages, the work represents an important step forward in making LiDAR-based navigation more reliable for real-world robotic applications. Su’s research addresses fundamental challenges in autonomous motion decision-making, with potential impacts on fields ranging from autonomous driving to mobile robotics. His innovative clustering-directed approach demonstrates a sophisticated understanding of how to extract meaningful geometric information from sparse 3D sensor data, positioning him as an emerging contributor to the SLAM and robotics perception communities.
Research Focus
Key Achievements
Top Papers
- 1