Papers
2
Total Citations
9
H-Index
2
About
Yu Nakagawa is a researcher in autonomous robotics and computer vision, with a primary focus on self-localization and collision avoidance for multi-robot systems. His most cited work, "A Monte Carlo Localization based on Template Matching using an Omni-directional Camera" (2009, 6 citations), introduces a robust self-localization method for the RoboCup Middle-Size League. By replacing traditional colored landmarks with template matching of field lines and the center circle, Nakagawa’s approach enhances localization accuracy in dynamic, unstructured environments—a key contribution to the RoboCup community. His earlier paper, "Collision Estimation from Information of an Omni-directional Camera" (2006, 3 citations), adapts maritime radar-based collision-avoidance calculations to omni-directional camera images, enabling robots to estimate the Closest Point of Approach (CPA) for safer navigation. This cross-domain innovation demonstrates his ability to transfer principles from ship navigation to ground robotics. While his citation counts are modest, Nakagawa’s work is notable for its practical integration of omni-directional vision with probabilistic methods, advancing the reliability of autonomous systems in competitive and real-world settings. His research remains relevant for students and engineers working on vision-based robot localization and multi-agent coordination.
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