Papers
1
Total Citations
11
H-Index
1
About
Yicong Chang is a researcher in computer vision and robotics, with a primary focus on autonomous navigation and perception in challenging indoor environments. Their most cited work, "Indoor Obstacle Discovery on Reflective Ground via Monocular Camera" (2023), addresses a critical yet underexplored problem: detecting obstacles on reflective surfaces—such as polished floors or glass—where traditional monocular depth estimation often fails. By developing a novel approach that leverages visual cues to distinguish real obstacles from misleading reflections, Chang has contributed a practical solution for safer robot and drone navigation in real-world settings. This paper has already garnered 11 citations, signaling its relevance to the growing field of robust visual perception. Chang’s work is particularly notable for its emphasis on low-cost, monocular camera systems, making their methods accessible for widespread deployment. Their research bridges the gap between theoretical computer vision and applied robotics, offering tangible improvements for autonomous systems operating in complex, human-centric spaces. As the demand for reliable indoor navigation grows, Chang’s contributions are poised to influence both academic research and industrial applications in assistive robotics and smart environments.
Research Focus
Key Achievements
Top Papers
- 1Indoor Obstacle Discovery on Reflective Ground via Monocular Camera11 citations · 2023