Masashi Yokozuka
National Institute of Advanced Industrial Science and Technology
Papers
11
Total Citations
80
H-Index
6
About
Masashi Yokozuka is a robotics researcher whose work spans autonomous mobile robotics, localization and mapping, path planning, and human-robot interaction. Over more than a decade, he has made sustained contributions to the challenge of enabling robots and intelligent vehicles to navigate complex, real-world environments reliably and safely. His early work focused on autonomous robotic wheelchairs designed to assist elderly users in urban settings, combining obstacle avoidance with socially aware path planning — research that earned recognition for its human-centered engineering approach. He developed energy-minimizing path planning algorithms that allow mobile robots to travel smoothly while anticipating pedestrian motion, a practically significant advancement for robots sharing human spaces. More recently, Yokozuka has emerged as a leading contributor to sensor fusion and odometry research, with highly regarded work on tightly-coupled LiDAR-IMU-wheel and LiDAR-IMU-leg odometry systems. His innovative use of online neural kinematic model learning via factor graph optimization addresses the longstanding challenge of LiDAR degeneration in featureless environments such as tunnels and corridors — work already accumulating strong citation momentum. He has also contributed to human attention mapping using eye-tracking and to forest road detection using LiDAR-SLAM and deep learning. Collectively, his publications have garnered over 70 citations, reflecting broad influence across robotics research communities.
Research Focus
Key Achievements
Top Papers
- 1
- 2A Reasonable Path Planning via Path Energy Minimization12 citations · 2014
- 34D Attention: Comprehensive Framework for Spatio-Temporal Gaze Mapping11 citations · 2021
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- 7Forest road surface detection using LiDAR-SLAM and U-Net5 citations · 2021
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