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About

Xingming Wu is a robotics and computer vision researcher whose work spans mobile robotics, 3D perception, human-robot interaction, and rehabilitation engineering. His most influential contributions center on leveraging depth-sensing technologies—particularly the Microsoft Kinect—for real-world robotic applications. His 2013 paper on fast 3D modeling in complex environments using a single Kinect sensor has garnered 36 citations, while his complementary work on indoor localization and 3D scene reconstruction established foundational methods for simultaneous pose estimation and dense environment mapping. Wu has also made notable contributions to outdoor robotics, exploring visual terrain classification approaches that help mobile robots navigate unstructured environments. Beyond perception, Wu's research extends into cable-driven humanoid robotics, where he analyzed the stiffness and dynamics of 3-DOF spherical joints mimicking human shoulder motion. His later work broadened into deep learning-based semantic segmentation, multi-sensor depth fusion combining LiDAR, ToF cameras, and binocular vision, and hexapod locomotion control on uneven terrain. Notably, Wu has applied his robotics expertise to healthcare, developing a virtual reality training system for upper limb stroke rehabilitation. Across a decade of diverse contributions, his cumulative impact reflects a sustained commitment to bridging intelligent perception, mechanical design, and practical robotic deployment.