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About

Eryong Wu is a robotics and non-destructive testing researcher whose work spans two distinct but complementary domains: autonomous robot navigation and advanced ultrasonic imaging for industrial inspection. With a career rooted in computer vision and probabilistic robotics, Wu has made significant contributions to simultaneous localization and mapping (SLAM), developing novel algorithms that enable mobile robots to navigate complex, unknown outdoor environments using stereo and monocular cameras. His early work on Rao-Blackwellised particle filter-based SLAM and Extended Kalman Filter approaches addressed critical computational challenges, improving both localization accuracy and robustness while reducing particle degeneracy — problems central to real-world autonomous systems. In later years, Wu pivoted toward robot-assisted ultrasonic testing, pioneering track-scan imaging methods for detecting defects in curved and complexly structured components. His most cited work, the VGG-UNet deep learning framework for ultrasonic image reconstruction (38 citations), demonstrates a forward-thinking integration of neural networks into industrial non-destructive evaluation, substantially improving defect detection sensitivity in challenging geometries. Across his publication record, Wu exemplifies how foundational robotics expertise — in sensing, perception, and localization — can translate powerfully into precision manufacturing and inspection applications, making his research relevant to engineers and roboticists alike.