Jiemao Wen
Papers
1
Total Citations
3
H-Index
1
About
Jiemao Wen is a researcher whose work centers on advancing mobile robot localization through innovative neural network architectures. His primary contributions lie at the intersection of probabilistic filtering and deep learning, specifically addressing critical limitations in Differentiable Particle Filters (DPFs). In his most cited work, "Mobile Robot Localization Based on Gradient Propagation Particle Filter Network" (2020), Wen tackles a fundamental challenge: the non-differentiability of the resampling process in end-to-end DPF training, which prevents gradient information from flowing backward through the network. By proposing a novel gradient propagation mechanism, he enables more effective training of these hybrid models, improving their accuracy and robustness for real-world localization tasks. While his citation count is currently modest, his work represents a meaningful step toward bridging classical Bayesian filtering with modern deep learning—a key frontier in robotics. Wen’s research is particularly relevant for students and engineers working on autonomous systems, sensor fusion, and differentiable programming, offering a practical solution to a persistent technical bottleneck in mobile robot navigation.
Research Focus
Key Achievements
Top Papers
- 1