Papers
4
Total Citations
36
H-Index
3
About
Yuanmin Xie is a robotics researcher whose work centers on perception, manipulation, and localization for intelligent systems, with a particular focus on substation inspection robots and autonomous ground vehicles. Xie’s most impactful contribution is the development of a digital twin model for robot grasping in cluttered, multi-object environments, a paper that has already garnered 20 citations since 2023. This work addresses a critical challenge in industrial automation by enabling robots to plan grasps within a simulated stacking environment. Complementing this, Xie proposed a multi-scale feature fusion network for substation instrumentation detection, achieving high-precision target recognition that is essential for smart grid inspection—a paper cited 12 times. More recently, Xie introduced Ground-LIO, a LiDAR-inertial odometry system that optimizes pose estimation by fully leveraging ground point clouds, a novel approach that enhances localization accuracy for ground robots. Additionally, Xie’s work on a depth-aware, learnable feature fusion network advances geometric perception for semantic correspondence, supporting downstream tasks like robot manipulation and pose estimation. Through these contributions, Xie has demonstrated a clear trajectory of innovation in sensor fusion, scene understanding, and robotic autonomy, with a growing citation footprint that underscores the practical relevance of their research.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4