Xingbin Chen
Papers
1
Total Citations
9
H-Index
1
About
Xingbin Chen is a leading researcher in the field of visual simultaneous localization and mapping (V-SLAM), with a particular focus on enabling robust autonomous navigation in highly dynamic environments. His most impactful work, "ADM-SLAM: Accurate and Fast Dynamic Visual SLAM with Adaptive Feature Point Extraction, Deeplabv3pro, and Multi-View Geometry" (2024), has already garnered 9 citations, signaling its rapid influence. Chen’s major contribution lies in overcoming a critical bottleneck in V-SLAM: the degradation of accuracy caused by moving objects. By integrating adaptive feature point extraction with a novel Deeplabv3pro segmentation model and multi-view geometry, his ADM-SLAM system achieves both high precision and real-time performance in cluttered, unpredictable settings. This work directly advances the capabilities of intelligent robotics and autonomous systems, offering a practical solution for applications ranging from service robots to self-driving vehicles. Chen’s research is distinguished by its elegant fusion of deep learning and classical geometric methods, setting a new benchmark for dynamic SLAM and establishing him as a rising authority in the field.
Research Focus
Key Achievements
Top Papers
- 1