Yubao Liu

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About

Yubao Liu is a researcher working at the intersection of robotics, computer vision, and intelligent systems, with a particular focus on advancing the capabilities of autonomous robots in complex, real-world environments. His work spans visual simultaneous localization and mapping (SLAM), multi-robot coordination, and digital twin frameworks, reflecting a broad yet cohesive research vision centered on making robotic systems more adaptive and reliable. Among his notable contributions is KMOP-vSLAM, a dynamic visual SLAM system designed for RGB-D cameras that leverages K-means clustering and OpenPose to overcome the longstanding scene rigidity assumption — a critical limitation that has historically restricted SLAM deployment in environments populated by moving objects. This work, which has garnered 14 citations, represents a meaningful step forward for applications in smart robotics and augmented reality. Liu has also explored the frontier of multi-robot systems through digital twin prediction frameworks incorporating terahertz (THz) communication, addressing the intricate challenges of transporting deformable linear objects — a problem of considerable complexity due to metamorphic constraints. With 10 citations, this contribution highlights his engagement with emerging communication technologies and collaborative robotic manipulation, positioning him as a forward-thinking researcher shaping the future of intelligent, connected robotic systems.