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About

Masatoshi Okutomi is a robotics and computer vision researcher whose work spans four decades of foundational contributions to autonomous robot navigation, stereo vision, and visual localization. He is perhaps best known for his pioneering development of potential field methods for robot movement planning, first introduced in 1983 and significantly expanded in a 1986 paper that has since accumulated 60 citations — establishing core concepts such as "state space for a robot" and "oval potential" that influenced subsequent generations of motion planning research. His 1990 work on Bayesian foundations for active stereo vision demonstrated an early and sophisticated approach to probabilistic 3D sensing, addressing reliability challenges that remained central to the field for years. Okutomi has also made meaningful contributions to applied robotics, including stereo-based floor sensing for biped robots, real-time step edge estimation, and ground surface reconstruction from stereo imagery. More recently, his 2019 research on geometric-semantic pose verification for indoor visual localization, garnering 45 citations, reflects his continued relevance at the intersection of computer vision and augmented reality. His body of work, bridging classical motion planning and modern perception, makes him a notable figure in the evolution of intelligent robotic systems.