Papers
1
Total Citations
4
H-Index
1
About
Yu Gan is a researcher specializing in robotics and autonomous systems, with a particular focus on improving Simultaneous Localization and Mapping (SLAM) performance. His most notable contribution is the development of a dynamic detection method that enhances the robustness and accuracy of SLAM algorithms in real-world environments. This work, published in 2021 and garnering 4 citations, addresses a critical challenge in robotics: enabling autonomous systems to operate reliably in dynamic, unpredictable settings by filtering out moving objects that degrade mapping quality. While his citation count is modest, the impact of this research lies in its practical applicability to fields such as autonomous navigation, drone technology, and mobile robotics. Gan’s approach has been recognized for its potential to advance SLAM systems beyond static, controlled environments, making it a valuable reference for engineers and researchers seeking to deploy robots in complex, human-populated spaces. His work underscores a commitment to bridging the gap between theoretical algorithms and real-world deployment, positioning him as an emerging contributor to the robotics community.
Research Focus
Key Achievements
Top Papers
- 1A dynamic detection method to improve SLAM performance4 citations · 2021