Haowei Zhang
Papers
1
Total Citations
2
H-Index
1
About
Haowei Zhang is a researcher whose work sits at the intersection of robotics, probabilistic estimation, and deep learning, with a particular focus on advancing how mobile robots perceive and navigate their environments. His most notable contribution is a novel framework that unifies classic trajectory estimation with modern unsupervised deep learning, enabling robots to extract rich features from LiDAR data without requiring labeled training data. This approach, detailed in his 2021 paper on unsupervised learning of LiDAR features for probabilistic trajectory estimators, extends existing system identification methods by embedding deep sensor processing within a Gaussian variational inference setting—all optimized under a single learning objective. While still early in its citation impact, this work represents a significant conceptual bridge between traditional robotics estimation and data-driven perception. Zhang’s research is especially relevant for students and engineers working on autonomous navigation, sensor fusion, and self-supervised learning, as it demonstrates how to leverage the strengths of both classical probabilistic methods and modern deep learning to build more robust, adaptive robotic systems.
Research Focus
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Top Papers
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