Papers
1
Total Citations
5
H-Index
1
About
Xinya Li is a robotics researcher whose work focuses on advancing Simultaneous Localization and Mapping (SLAM) for dynamic, real-world environments. Li’s key research area lies in developing robust visual SLAM systems that can operate reliably in scenes with moving objects—a critical challenge for autonomous mobile robots. Their most notable contribution, the "Inpainting SLAM" approach, introduces a novel method for detecting and recovering regions corrupted by dynamic objects, effectively "inpainting" these areas to maintain accurate map construction and localization. This work directly addresses the limitations of traditional SLAM systems that assume static scenes, which often fail in cluttered or populated spaces. Though early in its impact, the paper has already garnered 5 citations, signaling growing interest in this practical solution. Li’s research bridges the gap between theoretical SLAM algorithms and real-world deployment, offering a pathway for robots to navigate safely in environments like warehouses, hospitals, or public spaces. By tackling the dynamic-object problem head-on, Li is helping to make autonomous navigation more resilient and trustworthy—a vital step toward widespread robotic integration.
Research Focus
Key Achievements
Top Papers
- 1