Papers
1
Total Citations
2
H-Index
1
About
Qi Teng is a researcher focused on advancing robotic perception and manipulation, particularly through the integration of vision and depth data. Their key research areas include robot arm control, 3D pose estimation, and RGB-D data processing for automated picking tasks. Teng’s major contribution is the development of a novel extraction method for position and posture information of robot arms using RGB-D data, addressing significant errors in traditional approaches. This work, published in 2020, proposes a technique that leverages both color and depth information to accurately determine the target’s orientation during grasping, enhancing the precision and reliability of robotic manipulation in dynamic environments. While the paper has garnered 2 citations, its impact lies in its practical application to industrial automation and robotics, offering a foundation for further improvements in real-time object handling. Teng’s research is notable for its focus on reducing computational errors in pose estimation, a critical challenge in fields like manufacturing and logistics. Their work contributes to the broader goal of enabling robots to interact more effectively with unstructured surroundings, making it a valuable reference for students and researchers exploring vision-based robotic control.
Research Focus
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Top Papers
- 1