Papers
2
Total Citations
17
H-Index
2
About
Yunfei Xu is a researcher whose work lies at the intersection of robotics, sensor networks, and probabilistic machine learning, with a primary focus on environmental field reconstruction. His key research areas include Bayesian prediction, adaptive sampling, and spatio-temporal modeling for mobile sensor networks. Xu’s major contributions center on developing fully Bayesian algorithms that enable mobile robotic sensors to intelligently reconstruct environmental fields—such as temperature or pollution gradients—even when sensor localization is uncertain. In his most-cited work, “Bayesian Prediction and Adaptive Sampling Algorithms for Mobile Sensor Networks” (2015, 15 citations), he introduced a framework for online spatio-temporal field reconstruction that allows robots to dynamically decide where to sample next, maximizing information gain. Building on this, his 2018 paper advanced the state of the art by incorporating Gaussian Markov random fields to handle uncertain localization, a critical step toward deploying robots in GPS-denied or cluttered environments. Though his citation counts are modest, Xu’s contributions are foundational for researchers working on autonomous environmental monitoring, demonstrating how principled Bayesian methods can turn noisy, sparse sensor data into accurate, real-time maps of the physical world.
Research Focus
Key Achievements
Top Papers
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