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About

Qingwen Xu is a robotics researcher whose work lies at the intersection of underwater autonomy, visual perception, and terrain-adaptive locomotion. Her research focuses on solving fundamental challenges in localization and depth estimation for robots operating in extreme environments. Xu's most impactful contribution is an adaptive navigation scheme for deep-sea localization that fuses multimodal perception cues, enabling safer and more reliable underwater robot interventions—a critical advance for tasks like floating manipulation and seafloor mapping (13 citations). She has also pioneered novel approaches to visual odometry, including rethinking the Fourier-Mellin Transform to handle feature-deprived or repetitive environments (12 citations), and developing unsupervised monocular depth estimation for spherical underwater imagery using in-air RGB-D data (7 citations). On land, Xu has advanced rescue robotics through configuration-space flipper planning on 3D terrain, enabling tracked robots to autonomously navigate unstructured disaster zones (6 citations). Her work on rotation estimation for omni-directional cameras using sinusoid fitting further demonstrates her versatility in sensor-based perception. With a growing citation record and contributions spanning both aerial and underwater domains, Xu is establishing herself as a rising figure in field robotics, pushing the boundaries of what autonomous systems can achieve in the world's most challenging environments.