Xiaoliang Yang
Papers
1
Total Citations
3
H-Index
1
About
Xiaoliang Yang is a researcher focused on advancing autonomous robot perception through binocular stereo vision and depth imaging techniques. Their work addresses a critical challenge in dynamic environments: the formation of depth holes in disparity images caused by unmatched points during stereo matching. Yang’s most cited paper, "Depth hole filling and optimizing method based on binocular parallax image" (2023), proposes a novel approach to detect and fill these gaps, enhancing the completeness and accuracy of depth maps—a fundamental requirement for robots navigating complex, real-world settings. With 3 citations, this contribution underscores Yang’s role in refining environment perception algorithms that enable safer and more reliable autonomous operations. By tackling the persistent issue of incomplete depth data, Yang’s research supports broader advancements in robotics, from obstacle avoidance to object manipulation. Their work exemplifies the meticulous engineering needed to bridge the gap between theoretical computer vision and practical robotic systems, making Yang a notable contributor to the field of autonomous perception.
Research Focus
Key Achievements
Top Papers
- 1