Papers
2
Total Citations
7
H-Index
2
About
Xiaohang Zhou is a robotics researcher whose work lies at the intersection of human-machine interaction and autonomous navigation. Zhou’s key contributions focus on enabling more natural coordination between humans and robots in smart manufacturing environments, as well as improving how robots perceive and map their surroundings. In a foundational 2019 paper (5 citations), Zhou developed an improved optical flow method for a body-following wheeled robot using Kinect sensors, integrating gesture recognition to allow intuitive human-robot collaboration—a critical capability for future smart factories. More recently, Zhou has tackled the persistent challenge of semantic deficiency in visual SLAM systems. In a 2025 paper (2 citations), Zhou proposed a DeepLabV3+-based semantic annotation refinement method that enhances 3D scene reconstruction from monocular imagery, significantly boosting robotic operational efficiency in indoor environments. By addressing both the interaction and perceptual bottlenecks in robotics, Zhou’s work bridges the gap between human intent and machine understanding, offering practical solutions for autonomous systems operating in complex, real-world settings.
Research Focus
Key Achievements
Top Papers
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