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About

Yuriko Ueda is a robotics researcher specializing in visual navigation and semantic segmentation for autonomous mobile robots operating in human-centric environments. Her work addresses the critical challenge of enabling robots to navigate safely and effectively alongside pedestrians, particularly through the development of robust perception systems. Ueda’s major contributions center on creating and augmenting specialized datasets for semantic segmentation, a technique that provides pixel-wise class labels to help robots understand their surroundings. Recognizing that public datasets often fail in real-world applications, she has pioneered methods for generating custom datasets using colored point clouds and 3D scanned data, while also developing area-wise augmentation techniques to improve model accuracy under varying conditions. Her most cited paper (14 citations) demonstrates a practical implementation of semantics-based localization that allows robots to perform expected actions without being hindered by pedestrian presence. Ueda’s research is closely tied to the Tsukuba Challenge, a prestigious autonomous robot competition in Japan, where she has tested her approaches in both outdoor and indoor environments. Her work on data augmentation and dataset creation directly addresses the practical limitations of deploying visual navigation systems in the wild, making her contributions valuable for advancing real-world robotic autonomy.