Papers
2
Total Citations
5
H-Index
2
About
Xianyu Shi is a leading researcher in the fields of field robotics and intelligent perception, with a core focus on advancing autonomous navigation in complex, unstructured environments. His major contributions lie in developing robust, multi-sensor systems for geospatial data collection and terrain understanding. Notably, his work on "Multirobot Collaborative SLAM Based on Novel Descriptor With LiDAR Remote Sensing" (2024, 3 citations) pioneers a method for multiple robots to collaboratively construct environmental maps using 3-D LiDAR, dramatically improving the efficiency of geospatial data acquisition for urban planning and environmental sustainability. Additionally, his research on "A Terrain Recognition Method Based on Semantic Segmentation for Field Robot Under Sample Imbalance" (2023, 2 citations) addresses a critical gap by enabling robots to recognize and adapt to complex off-road terrains, overcoming the limitations of urban-focused models. By tackling sample imbalance in semantic segmentation, Shi’s work ensures reliable performance in real-world, data-scarce scenarios. His achievements are foundational for the next generation of field robots, from agricultural automation to disaster response, and his innovative integration of remote sensing and collaborative robotics marks him as a pivotal figure in intelligent autonomous systems.
Research Focus
Key Achievements
Top Papers
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- 2