Xiaoming Xie
Papers
1
Total Citations
11
H-Index
1
About
Xiaoming Xie is a leading researcher in visual simultaneous localization and mapping (SLAM), with a particular focus on enabling robust autonomous navigation in complex, dynamic environments. His most impactful work, "USD-SLAM: A Universal Visual SLAM Based on Large Segmentation Model in Dynamic Environments," introduces a groundbreaking framework that leverages large-scale segmentation models to achieve precise pose estimation even in highly unpredictable settings. This contribution directly addresses a critical bottleneck in autonomous driving and robotics, where traditional SLAM systems fail when confronted with moving objects and changing scenes. With over 11 citations since its 2024 publication, Xie’s research is rapidly gaining recognition for its practical utility and theoretical depth. By bridging the gap between static assumptions and real-world dynamism, he is helping to lay the foundation for more reliable and versatile robotic perception. Xie’s work stands out for its innovative integration of advanced segmentation techniques into the SLAM pipeline, offering a universal solution that promises to advance the capabilities of autonomous systems across diverse applications.
Research Focus
Key Achievements
Top Papers
- 1