Lele Bai
Papers
2
Total Citations
76
H-Index
2
About
Lele Bai is a robotics and computer vision researcher whose work centers on advancing autonomous mobile robot navigation through innovative approaches to Visual Simultaneous Localization and Mapping (VSLAM). Recognizing a critical limitation in conventional VSLAM systems — their reliance on rigid scene assumptions that break down in real-world dynamic environments — Bai has dedicated significant effort to developing more robust solutions capable of handling independently moving objects in indoor settings. Bai's two most prominent contributions, SGC-VSLAM and MGC-VSLAM, both published in 2020, tackle this challenge from complementary angles. SGC-VSLAM integrates semantic and geometric constraints to filter dynamic elements and improve localization accuracy, while MGC-VSLAM employs a meshing-based approach combined with geometric constraints to achieve similar goals through a distinct technical pathway. Together, these works have accumulated over 76 citations, reflecting their meaningful influence on the VSLAM research community. For students and researchers working on robot perception, autonomous navigation, or scene understanding, Bai's research offers practical and theoretically grounded frameworks for deploying VSLAM in the cluttered, unpredictable environments that real-world robotics demands. His contributions represent an important step toward making autonomous robots more reliable beyond controlled laboratory conditions.
Research Focus
Key Achievements
Top Papers
- 1
- 2