Yucheng Gao

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About

Yucheng Gao’s research lies at the intersection of robotics, computer vision, and intelligent manufacturing, with a focus on enabling autonomous systems to perceive and interact with their environments more accurately. His major contributions include the development of a dense mapping RGB-D SLAM system that integrates nonlinear optimization and keyframe selection, allowing mobile robots to simultaneously localize and construct visualizable pointcloud maps in real time. This work, cited 6 times, addresses critical challenges in simultaneous localization and mapping (SLAM) for unstructured environments. In parallel, Gao has advanced robotic precision in aerospace manufacturing through his work on optimizing normal direction measurement for robotic drilling. His 2014 paper, with 5 citations, tackles the problem of ensuring perpendicularity in bolting holes on complex curved surfaces—a key factor in fatigue life and fluid dynamics—by compensating for manufacturing errors that render CAD models insufficient. Together, these contributions demonstrate Gao’s ability to bridge theoretical optimization with practical robotic applications, making his work valuable for researchers in autonomous navigation and industrial automation.