Tomoya Sato

Papers

Total Citations

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About

Tomoya Sato is a researcher working at the intersection of computer vision and autonomous systems, with a particular focus on robust localization and mapping technologies for real-world deployment. His work addresses one of the most pressing challenges in autonomous driving: ensuring reliable performance under adverse environmental conditions. His most notable contribution, "Vision-based Localization using a Monocular Camera in the Rain" (2019), tackles the degradation of vision-based simultaneous localization and mapping (SLAM) systems when exposed to challenging weather conditions such as rain. Traditional approaches like ORB-SLAM rely on light intensity features that are highly susceptible to weather-induced noise, and Sato's research proposes methods to improve robustness in these scenarios using a monocular camera setup. This work has garnered 3 citations, reflecting its emerging relevance in a specialized but rapidly growing field. As autonomous vehicles move closer to widespread adoption, Sato's contributions to weather-resilient perception systems position him as a meaningful voice in the conversation around dependable autonomous navigation. His research is particularly valuable for students and engineers seeking to bridge the gap between laboratory-grade SLAM systems and real-world operational reliability.