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About

Liguan Wang is a researcher at the forefront of autonomous systems and robotics, with a primary focus on Simultaneous Localization and Mapping (SLAM) and intelligent automation for challenging environments. His most impactful work, a comprehensive review on dynamic object filtering in 3D LiDAR-based SLAM (2024, 23 citations), addresses a critical bottleneck in autonomous navigation—how to reliably distinguish moving objects from static surroundings in real-world, dynamic scenes. This contribution is foundational for advancing the safety and robustness of autonomous driving, mobile robotics, and UAVs. Wang also pioneers the application of robotics in mining, where he has developed a modeling and simulation framework for unmanned driving of load-haul-dump vehicles (2022) and proposed MAMRS, an innovative automatic meter reading system that leverages quadruped robots and improved deep learning algorithms (2024). By integrating cutting-edge perception, simulation, and robotic manipulation, his work directly tackles the inefficiencies and hazards of manual operations in underground mines. With a growing citation footprint, Wang is establishing himself as a key innovator in bridging theoretical SLAM research with practical, high-impact industrial automation.