Wataru Yoshiuchi
Papers
2
Total Citations
8
H-Index
2
About
Wataru Yoshiuchi is a robotics researcher focused on advancing autonomous navigation and perception for mobile robots operating in unstructured environments. His work centers on two key challenges: enabling reliable robot self-localization without extensive prior data collection, and robustly estimating camera attitude under dynamic body movements. In his 2020 study, Yoshiuchi proposed a novel navigation framework that leverages an edge-node map created directly from electronic maps, eliminating the need for prior sensor data collection required by traditional occupancy grid or 3D point cloud methods. This approach significantly reduces deployment overhead for autonomous systems. His 2022 work tackles camera attitude estimation by introducing a classification-based neural network, inspired by optical character recognition, as an alternative to conventional numerical regression. This method proves particularly valuable for terrestrial robots with freely tilting upper bodies, where IMU-based estimation alone is insufficient. While his most-cited papers each have 4 citations, Yoshiuchi’s contributions are notable for their practical focus on reducing system complexity and improving robustness in real-world robotic applications.
Research Focus
Key Achievements
Top Papers
- 1
- 2