Papers
34
Total Citations
256
H-Index
9
About
Ryuichi Ueda is a leading researcher in mobile robotics, with a primary focus on robust self-localization and sensor-based navigation. His most influential contribution is the development of the **expansion resetting method** for Monte Carlo Localization (MCL), a technique that enables robots to recover from fatal estimation errors—a critical advancement that has garnered over 42 citations. This work, alongside his **Uniform Monte Carlo localization** (26 citations), has significantly improved the computational efficiency and reliability of probabilistic localization in real-world environments. Ueda has also pioneered multi-sensor fusion for home robotics, notably integrating **RFID tags with ceiling cameras and particle filters** (32 citations) to achieve precise object localization. His research extends to optimal sensor placement for mobile robot trajectories (18 citations) and low-resource image processing using Discrete Wavelet Transforms (14 citations). A recurring theme in his work is addressing uncertainty—whether through vector quantization for state-action map compression (10 citations) or real-time decision-making under self-localization uncertainty (9 citations). Ueda’s contributions have been foundational for service robots operating in cluttered, dynamic spaces, making his methods essential reading for researchers in autonomous navigation and sensor fusion.
Research Focus
Key Achievements
Top Papers
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- 4Optimal camera placement considering mobile robot trajectory18 citations · 2009
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- 7Vector quantization for state-action map compression10 citations · 2004
- 8Quadruped Robot Navigation Considering the Observational Cost10 citations · 2002
- 9Recovery Methods for Fatal Estimation Errors on Monte Carlo Localization9 citations · 2005
- 10Real-Time Decision Making under Uncertainty of Self-localization Results9 citations · 2003