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About

Fanle Meng is a leading researcher in robotics and autonomous systems, with a primary focus on state estimation, surgical robotics, and human-robot interaction. His most impactful contribution is the development of FAST-LIVO2, a fast and direct LiDAR-inertial-visual odometry framework that has garnered 106 citations since its 2024 publication. This work enables real-time, robust state estimation for SLAM in demanding robotic applications, establishing Meng as a key innovator in multi-sensor fusion. In the domain of medical robotics, Meng has pioneered systems for stereoelectroencephalography (SEEG), including a surgical robot with augmented reality visualization and an automatic markerless registration method using optical cameras. These contributions, cited over 60 times collectively, enhance the precision and efficiency of depth electrode insertion for epilepsy treatment. More recently, Meng has advanced teleoperation through haptic sensor-aided variable impedance control and interactive semantic shared control frameworks, improving human-in-the-loop applications. His work bridges fundamental robotics challenges—from real-time perception to surgical assistance—demonstrating a sustained impact on both theoretical and applied robotics.