Yu-Teng Wei
Papers
1
Total Citations
23
H-Index
1
About
Yu-Teng Wei is a leading researcher in the field of robotics and computer vision, with a primary focus on simultaneous localization and mapping (SLAM) and stereo vision systems. His most impactful work, "SLAM-Based Self-Calibration of a Binocular Stereo Vision Rig in Real-Time" (2020), has garnered 23 citations and addresses a critical bottleneck in practical robotics: the cumbersome manual calibration of binocular stereo cameras. By integrating SLAM techniques, Wei pioneered a real-time, self-calibration method that eliminates the need for specific reference targets or offline procedures, enabling autonomous systems to maintain accuracy in dynamic environments. This contribution is particularly influential for applications in autonomous navigation, augmented reality, and industrial inspection, where continuous, reliable depth perception is essential. Wei’s work stands out for its elegant fusion of SLAM and calibration, offering a scalable solution that reduces human intervention and enhances system robustness. His research not only advances theoretical understanding but also provides practical tools for deploying stereo vision in real-world settings, making him a notable figure in the development of more autonomous and adaptive robotic systems.
Research Focus
Key Achievements
Top Papers
- 1SLAM-Based Self-Calibration of a Binocular Stereo Vision Rig in Real-Time23 citations · 2020