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Roger Mohr is a pioneering figure in computer vision, whose work has fundamentally shaped the fields of 3D reconstruction, camera calibration, and robotics vision. His research masterfully bridges the gap between theoretical geometry and practical robotic systems, focusing on how uncalibrated cameras can recover Euclidean structure from multiple images. Mohr’s most significant contribution is his systematic investigation of the constraints that make Euclidean reconstruction linear and tractable, particularly when cameras are mounted on robot arms. His seminal 1995 paper, "Understanding positioning from multiple images," with 19 citations, laid critical groundwork for projective-to-Euclidean conversion. In his highly influential 2002 work (9 citations), he addressed the challenge of Euclidean reconstruction with an uncalibrated affine camera, demonstrating how controlled robot motions could simultaneously achieve calibration and shape recovery. His earlier 1994 and 1995 papers on self-calibration of stereo heads and camera-mounted robot arms (6 and 4 citations, respectively) provided practical methodologies that remain relevant. Notably, his 1988 paper on matching 3-D images without backtracking introduced a coarse-to-fine approach for mobile robot environment modeling. Mohr’s legacy lies in making 3D vision computationally feasible for robotics, enabling machines to understand their environment with minimal calibration overhead.