Papers
147
Total Citations
2,983
H-Index
26
About
Jun Miura is a prominent robotics and computer vision researcher whose work spans mobile robot navigation, human-robot interaction, and simultaneous localization and mapping (SLAM). Over two decades of prolific research, he has made foundational contributions to how robots perceive, navigate, and interact within complex, dynamic environments. Miura's early work established key methods for autonomous robot navigation and sensor fusion, including integrating omnidirectional stereo with laser range finders for robust map generation — approaches that remain influential in mobile robotics. His research on person detection, tracking, and identification for service robots demonstrated practical pathways for robots to follow and assist specific individuals in real-world settings, drawing nearly 100 citations for his stereo-based tracking system alone. More recently, Miura has pushed the boundaries of visual SLAM in dynamic environments, with his RDS-SLAM and RDMO-SLAM frameworks — collectively accumulating nearly 400 citations — addressing the critical challenge of operating in populated, unpredictable spaces using semantic segmentation and optical flow. His 2019 LIDAR-based people behavior measurement system, his most-cited work with over 400 citations, exemplifies his commitment to building scalable, real-world systems that inform human-centered design. Across his career, Miura has consistently bridged perception, autonomy, and practical applicability in robotics research.
Research Focus
Key Achievements
Top Papers
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- 2RDS-SLAM: Real-Time Dynamic SLAM Using Semantic Segmentation Methods315 citations · 2021
- 3Object recognition supported by user interaction for service robots111 citations · 2003
- 4Autonomous visual navigation of a mobile robot using a human-guided experience102 citations · 2002
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