Wenxuan Chen

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About

Wenxuan Chen is a robotics researcher specializing in multi-sensor fusion, state estimation, and autonomous navigation for mobile robots. His work addresses critical challenges in enabling robots to perceive and operate reliably in complex, unknown environments. Chen's most influential contribution is "LVIO-SAM: A Multi-sensor Fusion Odometry via Smoothing and Mapping" (2021, 13 citations), which presents a robust framework that fuses LiDAR, visual, and inertial data for accurate real-time mapping and localization—a foundational capability for autonomous exploration. He has also advanced practical robot manipulation with "Online Hand-Eye Calibration with Decoupling by 3D Textureless Object Tracking" (2023), solving a key problem for visually guided grasping without requiring pre-calibration or fiducial markers. Additionally, his work "Meteor Tail: Octomap Based Multi-sensor Data Fusion Method" (2021) contributes to efficient 3D environment representation for autonomous navigation. Chen's research directly impacts the development of more capable, self-sufficient robots, bridging the gap between theoretical sensor fusion and real-world deployment in dynamic, unstructured settings.