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About

Satoki Ogiso is a robotics and signal processing researcher whose work centers on acoustic-based localization systems for mobile robots. His most significant contribution is the development of a low-cost self-localization method that leverages microphone arrays, wheel odometry, and acoustic beacons to enable precise robot positioning — a practical alternative to expensive sensor-based approaches. This foundational 2015 paper has garnered 39 citations, establishing him as a notable voice in the mobile robotics localization community. Building on this foundation, Ogiso has systematically refined his acoustic localization framework, tackling real-world challenges such as reverberant environments, signal parameter optimization, and motion-induced measurement errors. His 2018 work introduced iterative Bayesian filtering to improve robustness in acoustically complex spaces, while complementary studies evaluated how M-sequence signal parameters and microphone array velocity affect direction-of-arrival and time-difference-of-arrival accuracy. Together, these investigations demonstrate a rigorous, incremental approach to solving the practical limitations of acoustic beacon systems. Ogiso's research offers valuable insights for engineers and roboticists seeking affordable, reliable indoor localization solutions, making his body of work particularly relevant to the growing field of autonomous mobile robotics.