Ben Sargeant

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About

Ben Sargeant is a researcher whose work bridges the fields of photogrammetry, robotics, and 3D imaging. His primary research areas include automated 3D acquisition, close-range photogrammetry, and robotic calibration. Sargeant’s most notable contribution is his pioneering work on fully automatic and reliable 3D point cloud generation, as detailed in his highly cited 2014 paper, which has garnered 38 citations. This work addresses the critical challenge of designing an imaging network that yields complete and accurate 3D data without manual intervention. Additionally, his 2013 paper on a webcam photogrammetric method for robot calibration introduces an innovative strategy for calibrating a 5-DoF robot using two web cameras. By employing Denavit-Hartenberg parameters and close-range photogrammetry, Sargeant’s method ensures precise camera positioning relative to an object, enhancing robotic accuracy. His research has significant implications for automation, quality control, and 3D modeling. Sargeant’s work stands out for its practical, cost-effective approach, making advanced 3D acquisition and calibration accessible to a wider range of applications.