Minghui Lyu

About

Minghui Lyu is a leading researcher in intelligent navigation and multi-sensor fusion, with a primary focus on developing robust positioning systems for autonomous vehicles and mobile robots. His most-cited work, "Visual-Inertial-GNSS Fusion Positioning for Vehicles With Deep-Learning-Based Feature Extraction and Outlier Detection" (2025), addresses the critical challenge of maintaining high-precision localization in complex urban environments where GPS signals are often degraded. Lyu’s key contribution lies in integrating deep learning techniques with traditional sensor fusion—combining visual, inertial, and GNSS data—to enhance both feature extraction and outlier rejection, thereby improving system reliability under real-world conditions. Though early in its citation trajectory, this work has already garnered attention for its practical implications in autonomous navigation. Lyu’s research bridges the gap between theoretical algorithms and deployment-ready solutions, making him a notable figure in the fields of robotics, autonomous driving, and sensor fusion. His work is essential reading for students and engineers seeking to understand state-of-the-art approaches to resilient, multi-modal positioning in challenging environments.

Research Focus

Key Achievements

Top Papers

1. 1
   Visual-Inertial-GNSS Fusion Positioning for Vehicles With Deep-Learning-Based Feature Extraction and Outlier Detection

   2 citations · 2025

Key Collaborators

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