Takumi Matsuda
Papers
2
Total Citations
7
H-Index
2
About
Takumi Matsuda’s research lies at the intersection of robotics, computer vision, and multi-agent systems, with a focus on enabling autonomous navigation and environmental monitoring in complex indoor settings. His work addresses two critical challenges: robust camera pose estimation and scalable multi-robot coordination. In his most cited paper (2022, 4 citations), Matsuda introduces a novel approach to camera attitude estimation using a classification neural network, inspired by optical character recognition (OCR) techniques. By framing pose estimation as a classification task rather than numerical regression, his method achieves greater robustness for terrestrial robots with freely tilting upper bodies, where traditional IMU-based methods often falter. This work offers a practical solution for high-maneuverability robots requiring precise self-localization. Complementing this, Matsuda’s second most cited paper (2022, 3 citations) proposes a mutual positioning relay method for heterogeneous robot teams monitoring indoor environments. Here, he introduces a hierarchical system where a few high-performance “parent” robots assist numerous low-cost “child” robots in maintaining accurate positioning, enabling scalable and cost-effective surveillance. Together, these contributions advance both single-robot perception and swarm-level coordination, highlighting Matsuda’s ability to bridge theoretical innovation with real-world robotic applications.
Research Focus
Key Achievements
Top Papers
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- 2