Papers
14
Total Citations
726
H-Index
9
About
Kenji Koide is a leading researcher in robotics and autonomous systems, whose work is pivotal in advancing how machines perceive, track, and interact with people and their environments. His primary research areas span 3D LiDAR-based perception, human behavior analysis, and robust localization for mobile robots. Koide’s most influential contribution is the development of a portable 3D LiDAR system for long-term people behavior measurement, a foundational work cited over 400 times that enables the design of intelligent, interactive systems. He has also made critical advances in hand-eye calibration, introducing a novel technique based on reprojection error minimization that streamlines camera-robot coordination. In the domain of person-following robots, Koide has pioneered methods for identifying individuals using color, height, gait, and deep learning-based feature selection, achieving high re-identification accuracy. His recent work tackles the challenge of localization in feature-poor environments like tunnels, developing tightly-coupled LiDAR-IMU-wheel odometry with online kinematic model learning. With over 700 total citations, Koide’s research is essential reading for anyone working on autonomous navigation, human-robot interaction, and real-world robotic perception.
Research Focus
Key Achievements
Top Papers
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- 2General Hand–Eye Calibration Based on Reprojection Error Minimization102 citations · 2019
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- 5Large-scale 3D outdoor mapping and on-line localization using 3D-2D matching18 citations · 2017
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