Xiao Ji

Papers

Total Citations

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About

Xiao Ji is a researcher focused on advancing intelligent robotics and environmental perception technologies, with a particular emphasis on field robotics and real-time sensing systems. Their most cited work, "Field Robot Environment Sensing Technology Based on TensorRT" (2021), demonstrates a key contribution to optimizing deep learning inference for autonomous robots operating in complex outdoor environments. By leveraging NVIDIA's TensorRT framework, Ji's research addresses the critical challenge of balancing computational efficiency with accurate environmental perception—enabling robots to process sensor data in real-time for navigation and obstacle detection. This work has garnered 4 citations, reflecting its niche but growing influence in the field of embedded AI for robotics. Ji's broader research interests span sensor fusion, edge computing, and the deployment of lightweight neural networks on resource-constrained platforms. Their contributions are particularly relevant for agricultural robotics, autonomous surveying, and disaster response applications, where robust field performance is essential. As a researcher bridging the gap between advanced AI models and practical robotic systems, Xiao Ji's work continues to inform the development of more capable and responsive field robots.