Feiya Li
Papers
1
Total Citations
15
H-Index
1
About
Feiya Li is a leading researcher in the field of autonomous perception and 3D scene understanding, with a primary focus on semantic simultaneous localization and mapping (SLAM). Her work addresses a critical challenge in robotics and autonomous driving: enabling robust navigation in dynamic, real-world environments. Li’s most notable contribution is the development of SD-SLAM, a novel semantic SLAM approach that leverages LiDAR point clouds to filter out moving objects, thereby enhancing mapping accuracy and localization stability in cluttered scenes. This work, published in 2024, has already garnered 15 citations, reflecting its immediate relevance to the community. By integrating deep learning-based semantic segmentation with traditional geometric SLAM, Li’s research bridges the gap between high-level scene understanding and low-level sensor fusion. Her contributions are particularly impactful for applications in autonomous vehicles, where handling dynamic obstacles is essential for safety. Li’s work continues to push the boundaries of robust perception, making her a rising voice in the intersection of computer vision and robotics.
Research Focus
Key Achievements
Top Papers
- 1