Shili Chen
Papers
1
Total Citations
5
H-Index
1
About
Shili Chen has made significant contributions to the field of robotic perception and computer vision, with a particular focus on enabling robots to operate effectively in unstructured environments. Their most cited work introduces a pioneering framework that combines a single shot detector (SSD) with a refined LineMOD template matching approach for 3D object detection and pose estimation. This research directly addresses the critical challenge of improving a robot's ability to perceive and interact with multiple objects in complex, unpredictable settings—a fundamental requirement for advanced autonomous systems. By integrating deep learning with classical template matching, Chen's framework offers a robust solution for real-time object recognition and spatial understanding, achieving notable impact with 5 citations. This work stands as a key achievement in bridging the gap between theoretical computer vision and practical robotic applications, demonstrating Chen's expertise in sensor integration, algorithm design, and system engineering. Their research continues to influence developments in industrial automation, service robotics, and autonomous navigation, making Shili Chen a respected figure in the advancement of intelligent robotic systems.
Research Focus
Key Achievements
Top Papers
- 1