Kosuke Shigematsu
Papers
3
Total Citations
13
H-Index
2
About
Kosuke Shigematsu is a robotics researcher whose work focuses on enabling autonomous mobile robots to navigate safely and effectively in complex, human-populated environments. His primary research areas include deep learning-based perception for robotics, pedestrian and traffic light recognition, and agricultural robotics. Shigematsu has made significant contributions to the Tsukuba Challenge, an annual open experiment where robots must autonomously navigate urban settings amidst pedestrians and cyclists. His most cited paper, "Detection of Target Persons Using Deep Learning and Training Data Generation for Tsukuba Challenge" (2018, 7 citations), addresses the critical challenge of person detection in dynamic outdoor environments. In related work, "Recognition of Pedestrian Traffic Light at Crosswalk for a Mobile Robot Using Deep Learning" (2018, 4 citations), he developed a deep learning approach to enable robots to interpret traffic signals, a key capability for safe crosswalk navigation. Earlier in his career, Shigematsu explored agricultural robotics with a study on the annual utilization of a harvesting robot for strawberry forcing culture (2009, 2 citations). His research bridges perception and real-world deployment, advancing the reliability of autonomous systems in both urban and agricultural settings.
Research Focus
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Top Papers
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