Ueda Takeshi

Papers

Total Citations

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About

Ueda Takeshi’s research lies at the intersection of robotics, localization, and large-scale mapping, with a focus on enabling robots to navigate and understand expansive environments. His major contributions center on developing scalable inference algorithms for mobile robot localization, particularly through the use of high-dimensional features and landmark maps. In his most-cited work, “On the scalability of robot localization using high-dimensional features” (2008, 7 citations), he demonstrated how approximate nearest neighbor (ANN) retrieval can enhance map-matching performance in large-scale settings—a critical step for real-world deployment. Building on this, his paper “LSH-RANSAC: Incremental Matching of Large-Size Maps” (2010, 3 citations) introduced a novel approach that allows robots to localize using maps incrementally built by other robots, addressing a key challenge in collaborative SLAM (Simultaneous Localization and Mapping). Though his citation counts are modest, his work is foundational for researchers tackling scalability in robotics, offering practical solutions for multi-robot systems. Takeshi’s achievements highlight his ability to bridge theoretical algorithms with applied robotics, making him a notable figure in the field of autonomous navigation.