Raimu Yokota
Papers
2
Total Citations
43
H-Index
2
About
Raimu Yokota is a robotics researcher whose work focuses on enabling autonomous robot navigation using only visual information, reducing reliance on expensive 3D LiDAR sensors. His key research areas include semantic segmentation, visual navigation, and dataset generation for mobile robotics. Yokota’s major contribution is a novel vision-based navigation scheme that leverages semantic segmentation results from a single monocular camera to guide robots through urban and indoor environments. His most cited paper, “Visual Navigation Based on Semantic Segmentation Using Only a Monocular Camera as an External Sensor” (2020, 38 citations), demonstrates how robots can achieve road-following and obstacle avoidance using only visual data, making autonomous movement more accessible for human-centric spaces. In related work, “Generation of Datasets for Semantic Segmentation from 3D Scanned Data to Train a Classifier for Visual Navigation” (2020, 5 citations), he addresses the critical challenge of creating high-quality training data by converting 3D scans into labeled datasets. Yokota’s research bridges the gap between cost-effective sensing and robust navigation, offering a practical pathway for deploying autonomous robots in everyday environments. His work is particularly notable for its potential to democratize robotics by replacing expensive hardware with intelligent software solutions.
Research Focus
Key Achievements
Top Papers
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