Papers
2
Total Citations
17
H-Index
2
About
Yuefeng Xi is a robotics researcher whose work focuses on autonomous exploration, sensor planning, and 3D perception. Their key contributions lie in developing intelligent algorithms that enable robots to efficiently navigate and understand unknown environments using depth sensors. In their highly cited 2022 work on "Active hand-eye calibration via online accuracy-driven next-best-view selection" (14 citations), Xi pioneered a method that allows robotic systems to autonomously select optimal viewpoints to improve calibration accuracy in real time—a critical capability for reliable manipulation and sensing. More recently, Xi introduced THP (Tensor-field-driven hierarchical path planning) in 2024, a novel framework that leverages tensor fields to guide autonomous scene exploration with depth sensors, addressing the fundamental challenge of limited field-of-view in unknown 3D environments. This work demonstrates Xi's ability to combine theoretical rigor with practical robotic applications, encoding depth information more effectively to enable efficient, autonomous navigation. With growing citation impact and a clear trajectory toward solving real-world robotics challenges, Yuefeng Xi is establishing themselves as an emerging voice in autonomous systems and sensor-driven exploration.
Research Focus
Key Achievements
Top Papers
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