Wenhao Lei

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About

Wenhao Lei is a robotics researcher whose work lies at the intersection of sensor fusion, deep learning, and autonomous navigation. His primary research focuses on magnetic-assisted localization for mobile robots and self-supervised learning approaches for SLAM (Simultaneous Localization and Mapping). Lei’s most cited paper, “A review on magnetic-assisted localization for mobile robots” (2025), has garnered 7 citations and provides a comprehensive survey of how magnetic field sensing can enhance robot positioning in GPS-denied environments. In his notable work “IMU and LIDAR Odometry Based on CNN and RNN Self-Supervised Learning and Attention Mechanism” (2024, 3 citations), Lei introduces a novel framework that fuses inertial and lidar data using self-supervised deep learning—specifically combining convolutional and recurrent neural networks with attention mechanisms—to solve odometry problems without requiring labeled training data. This approach allows robots to better understand their environment through their own sensor streams, advancing robust autonomous navigation. Lei’s contributions are particularly impactful for mobile robotics applications where traditional localization methods fail, and his work continues to influence the development of intelligent, self-aware robotic systems.