Xun Yuan

University of Science and Technology of China

Papers

Total Citations

H-Index

About

Xun Yuan is a leading researcher in the field of intelligent robotics and autonomous navigation, with a primary focus on advancing Simultaneous Localization and Mapping (SLAM) systems. His most cited work, "SaD-SLAM: A Visual SLAM Based on Semantic and Depth Information" (2020, 53 citations), tackles a critical limitation in traditional SLAM: its vulnerability to dynamic environments. By integrating semantic understanding with depth data, Yuan developed a robust framework that enables mobile robots to accurately map and localize themselves even in scenes cluttered with moving objects—a challenge that has long hindered real-world deployment. This contribution has been widely recognized, with his paper serving as a key reference for researchers seeking to enhance SLAM reliability in dynamic settings. Beyond this flagship work, Yuan’s research continues to push boundaries in sensor fusion and deep learning for robotics, earning him a reputation for bridging theoretical advances with practical applications. His work not only advances autonomous pathfinding but also inspires new directions in intelligent robot perception, making him a notable figure in the robotics community.