Yingxin Wang
Papers
3
Total Citations
8
H-Index
2
About
Yingxin Wang is a robotics researcher specializing in autonomous navigation, multi-robot systems, and sensor fusion for state estimation. Their work focuses on developing intelligent algorithms that enable robots to perceive, plan, and operate reliably in complex, dynamic environments. A key contribution is the integration of graph attention neural networks with hierarchical motion planning for multi-robot navigation, a novel approach that enhances coordination and safety in shared spaces. Wang also advanced moving object segmentation with MosViT, a vision transformer architecture designed to extract spatial-temporal information from LiDAR point clouds, addressing critical challenges in autonomous driving and robotics. Additionally, their M³LVI framework represents a significant achievement in multi-modal odometry, tightly coupling LiDAR, visual, and inertial data through a factor graph for high-accuracy, robust state estimation and mapping. While early in their career, with publications from 2022–2024, Wang’s work has already garnered citations, demonstrating growing impact. Their research bridges deep learning and classical robotics, offering practical solutions for real-world autonomy.
Research Focus
Key Achievements
Top Papers
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