Takeshi Saitoh
Papers
9
Total Citations
84
H-Index
6
About
Takeshi Saitoh is a robotics and computer vision researcher whose work spans autonomous mobile robot navigation, human-robot interaction, and distributed robotic systems. His most recognized contributions lie in developing vision-based navigation strategies for indoor mobile robots, particularly pioneering the "center following" approach, which enables robots to navigate corridor environments using only a single monocular camera — eliminating the need for prior environmental mapping. This elegant, low-cost methodology, explored across multiple publications from 2007 to 2009 and accumulating over 50 citations collectively, demonstrated that robust obstacle detection and path planning could be achieved with minimal hardware. Saitoh further extended this foundation to include autonomous following and return-to-origin capabilities, making practical human-following robots a tangible reality. His 2017 work on head pose estimation using convolutional neural networks reflects a natural evolution toward deep learning-based perception, garnering 21 citations and highlighting his adaptability to emerging AI techniques. More recently, Saitoh has tackled the challenges of multi-robot data transmission efficiency within ROS 2 architectures, addressing Quality of Service optimization for real-world robotic deployments. His career reflects a consistent commitment to making autonomous robots more practical, perceptive, and deployable in everyday indoor environments.
Research Focus
Key Achievements
Top Papers
- 1
- 2Head Pose Estimation Using Convolutional Neural Network21 citations · 2017
- 3Mobile robot with following and returning mode10 citations · 2009
- 4
- 5
- 6Mobile robot with following function and autonomous return function6 citations · 2009
- 7Mobile robot navigation by center following using monocular vision5 citations · 2007
- 8Monocular Vision based Indoor Mobile Robot5 citations · 2008
- 9