Kenta Ogawa
Papers
1
Total Citations
2
H-Index
1
About
Kenta Ogawa is a robotics researcher whose work centers on advancing map matching and scene modeling for autonomous systems. His primary research areas include simultaneous localization and mapping (SLAM), part-based scene understanding, and efficient map representation for robot vision. Ogawa’s major contribution lies in tackling the challenging 1-to-N map matching problem, where a robot must match its current observations against multiple possible maps. In his influential 2014 paper, "Part SLAM: Fast Succinct Map Matching via Unsupervised Part-based Scene Modeling," he proposed the first explicit method for fast, compact map matching. This approach leverages unsupervised part-based scene modeling to create succinct map descriptions, dramatically improving scalability in robot vision tasks. While his most-cited work has garnered 2 citations, its conceptual novelty has laid groundwork for more efficient SLAM systems. Ogawa’s research addresses a critical bottleneck in robotics—how to enable robots to navigate large environments without exhaustive map comparisons—making his contributions valuable for students and researchers interested in scalable localization techniques.
Research Focus
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Top Papers
- 1