Yusong Zhou

Qingdao University of Science and Technology

Papers

Total Citations

H-Index

About

Yusong Zhou is a researcher focused on advancing autonomous mobile robotics through innovations in visual SLAM (Simultaneous Localization and Mapping) and real-time perception systems. His work centers on developing lightweight, computationally efficient algorithms that enable robots to navigate and map environments with high accuracy and robustness. Zhou’s most cited paper, “A Lightweight Visual Odometry Based on LK Optical Flow Tracking” (2023), addresses a critical challenge in autonomous driving: balancing real-time performance with precise positioning. By leveraging Lucas-Kanade optical flow tracking, his approach reduces computational overhead while maintaining the reliability of feature-based visual SLAM systems—a key requirement for safe AMR operation. This work has garnered early attention with 4 citations, reflecting its relevance to the growing field of resource-constrained robotics. Zhou’s contributions are particularly significant for applications in warehouse logistics, service robots, and autonomous vehicles, where low-latency, accurate mapping is essential. His research bridges the gap between theoretical SLAM frameworks and practical deployment, offering solutions that prioritize both speed and dependability. As the demand for intelligent, self-navigating systems expands, Zhou’s focus on efficient visual odometry positions him as a promising voice in the next generation of robotics engineering.