Yoko Ogawa
Papers
2
Total Citations
13
H-Index
2
About
Yoko Ogawa is a robotics researcher whose work centers on autonomous navigation, environmental mapping, and visual localization for mobile robots. Her primary contributions lie in developing robust, vision-based systems that allow robots to understand and move through unknown environments with high precision. In her most cited work (2007, 10 citations), Ogawa introduced a method using trinocular vision to build accurate 3-D keypoint maps based on SIFT features, enabling a mapping robot to construct a detailed environmental model before a second robot with only monocular vision localizes itself within that map. This two-stage approach significantly improves mapping accuracy and self-localization reliability. She further advanced the field by proposing a landmark selection strategy (2011, 3 citations) that reduces computational load for real-time navigation, allowing robots to quickly identify and match only the most informative visual features. Ogawa’s research bridges the gap between high-fidelity 3D mapping and efficient, practical robot deployment, making her work valuable for applications in search-and-rescue, autonomous exploration, and service robotics. Her focus on SIFT-based feature tracking and landmark optimization continues to influence modern visual SLAM systems.
Research Focus
Key Achievements
Top Papers
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- 2