Gongxing Yu

Qingdao University of Science and Technology

Papers

Total Citations

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About

Gongxing Yu is a researcher advancing the frontiers of autonomous mobile robotics, with a primary focus on simultaneous localization and mapping (SLAM) and visual odometry. His most-cited work, "A Lightweight Visual Odometry Based on LK Optical Flow Tracking" (2023), addresses a critical challenge in the field: balancing accuracy with real-time performance for safe robot navigation. By leveraging Lucas-Kanade optical flow tracking, Yu developed a streamlined visual SLAM system that maintains the robustness of feature-point-based methods while significantly reducing computational overhead—a vital contribution for resource-constrained autonomous platforms. This work has already garnered 4 citations, signaling its growing influence among peers tackling similar efficiency problems. Yu’s research sits at the intersection of computer vision and robotics, where his innovations directly impact the reliability of AMRs in dynamic environments. His achievements underscore a commitment to practical, deployable solutions that bridge the gap between theoretical SLAM algorithms and real-world operational demands. For students and researchers exploring lightweight perception systems, Yu’s work offers a compelling blueprint for achieving high-accuracy localization without sacrificing speed.