Rumin Zhang

Southern University of Science and Technology

Papers

Total Citations

H-Index

About

Rumin Zhang is a researcher advancing the field of robotic perception, with a primary focus on visual simultaneous localization and mapping (VSLAM) and loop closure detection (LCD). Their work addresses a critical challenge in autonomous systems—correcting drift and accumulated errors that degrade navigation accuracy over time. Zhang’s most notable contributions include the development of TLCD, a transformer-based loop closure detection method that significantly improves the reliability of visual SLAM for applications ranging from sweeping robots to drones. Building on this, Zhang introduced TT-LCD, a tensorized-transformer approach optimized for edge computing, enabling efficient, real-time performance on resource-constrained devices—a key enabler for autonomous driving and metaverse technologies. With papers accumulating citations in the field, Zhang’s work is recognized for bridging advanced deep learning architectures with practical robotic systems. Their research not only enhances the robustness of autonomous navigation but also pushes the boundaries of deploying sophisticated AI models on edge platforms, making them a promising voice in modern robotics and intelligent systems.