Yinzhen Liu
Papers
1
Total Citations
5
H-Index
1
About
Yinzhen Liu is a researcher focused on advancing autonomous navigation and perception for intelligent mobile systems, with key contributions in Simultaneous Localization and Mapping (SLAM) under dynamic environments. Liu’s most cited work, "Simultaneous Localization and Mapping of Unmanned Vehicles under Dynamic Environments with YOLOv7" (2022), addresses a critical limitation of traditional SLAM systems—such as ORB-SLAM, LSD, and SVO—which assume static surroundings. By integrating the YOLOv7 object detection framework, Liu’s approach enables robust SLAM performance in real-world, dynamic settings, enhancing the reliability of unmanned vehicles and VR applications. While this paper has garnered 5 citations, it represents a foundational step toward more adaptive and resilient autonomous systems. Liu’s research bridges computer vision and robotics, tackling challenges in real-time mapping and localization that are vital for self-driving cars, drones, and augmented reality. This work underscores Liu’s commitment to pushing the boundaries of intelligent mobility, offering practical solutions for environments where static assumptions fail. For students and researchers, Liu’s contributions highlight the importance of fusing deep learning with classical robotics to achieve greater autonomy in complex, unpredictable scenarios.
Research Focus
Key Achievements
Top Papers
- 1