Lihang Chen
Papers
1
Total Citations
5
H-Index
1
About
Lihang Chen’s research centers on visual simultaneous localization and mapping (VSLAM), with a particular focus on improving closed-loop detection and localization accuracy for mobile robots operating in indoor environments. His most-cited work, “A Closed-loop Detection Algorithm for Online Updating of Bag-Of-Words Model” (2023), tackles a critical challenge in VSLAM: the degradation of loop closure performance over time due to static bag-of-words models. Chen proposes a novel algorithm that dynamically updates the visual vocabulary online using monocular camera data, significantly enhancing the robot’s ability to recognize revisited locations and reduce drift. This contribution has garnered 5 citations, reflecting its relevance to researchers working on robust, real-time navigation systems. By addressing the trade-off between computational efficiency and long-term accuracy, Chen’s work supports the deployment of autonomous robots in complex, changing indoor spaces. His approach offers a practical solution for improving map consistency without requiring expensive sensors, making it valuable for cost-sensitive applications. Chen’s ongoing efforts continue to advance the reliability of VSLAM systems, positioning him as a promising contributor to the field of mobile robotics and computer vision.
Research Focus
Key Achievements
Top Papers
- 1