Shingo Totoki
Papers
1
Total Citations
10
H-Index
1
About
Shingo Totoki is a robotics and computer vision researcher specializing in autonomous systems for disaster response and human-robot interaction. His most cited work, "Real-time obstacle detection in a darkroom using a monocular camera and a line laser" (2022, 10 citations), addresses a critical challenge in search-and-rescue operations: enabling robots to navigate low-visibility environments. Totoki’s key contribution lies in developing a cost-effective, real-time method that combines a single camera with a line laser to detect obstacles in complete darkness, significantly improving the speed and safety of rescue missions. This work underscores his focus on practical, deployable solutions for emergency scenarios. Beyond this, Totoki has explored human-robot collaboration, aiming to enhance robot autonomy in unstructured settings. With growing citation impact, his research is gaining traction among engineers and first responders. Totoki’s achievements include advancing sensor fusion techniques that balance computational efficiency with accuracy, making his methods accessible for real-world deployment. His work not only pushes the boundaries of robotic perception but also directly contributes to saving lives, marking him as a rising figure in field robotics and disaster technology.
Research Focus
Key Achievements
Top Papers
- 1