Shuhao Kang
Papers
3
Total Citations
85
H-Index
3
About
Shuhao Kang is a rising researcher in robotics and autonomous systems, whose work centers on sensor fusion, semantic perception, and 3D scene understanding. His major contributions lie in developing robust algorithms for cross-modality calibration and registration, critical for enabling autonomous platforms to perceive and navigate complex environments. Kang’s highly cited work, “SE-Calib,” introduces a semantic edge-based method for online LiDAR-camera boresight calibration in urban scenes, a practical solution for fusing geometric and optical data without artificial targets. He further advanced robotic perception with “Mobile-Seed,” a lightweight framework for joint semantic segmentation and boundary detection on edge computing units, achieving the precise delineation needed for real-time manipulation and mapping. His “CoFiI2P” paper tackles the fundamental challenge of image-to-point cloud registration with a coarse-to-fine correspondence strategy, improving global alignment for cross-modality data fusion. With over 85 citations across these key publications from 2023-2024, Kang’s work is already shaping practical solutions for autonomous driving, robotic grasping, and online sensor calibration, establishing him as a promising voice in the field of embodied AI and perception.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3