Papers
1
Total Citations
4
H-Index
1
About
Shukun Xue is a researcher specializing in autonomous navigation and sensor fusion for small-scale mobile robotics, with a particular focus on obstacle detection in complex outdoor environments. Their major contribution lies in developing lightweight, computationally efficient perception systems that overcome the constraints of limited load capacity and processing power inherent in small ground robots. Xue's most cited work, "Obstacle detection based on image and laser points fusion for a small ground robot" (2015), introduces a novel method that integrates laser point cloud data with visual imagery using fuzzy clustering algorithms, enabling reliable obstacle detection without demanding high-end hardware. This approach has garnered 4 citations, establishing a foundation for resource-constrained robotic perception. Xue's research addresses a critical gap in field robotics, where traditional sensor fusion techniques often prove too heavy for compact platforms. Their work is particularly notable for its practical applicability in real-world scenarios, balancing algorithmic sophistication with operational feasibility. By pioneering fusion strategies that maximize the utility of limited onboard sensors, Xue has contributed to advancing the autonomy of small ground robots used in search-and-rescue, environmental monitoring, and agricultural applications.
Research Focus
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Top Papers
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