Ryoichi Ishikawa
Papers
8
Total Citations
30
H-Index
3
About
Ryoichi Ishikawa is a robotics researcher whose work centers on sensor fusion, calibration, and autonomous navigation, with a particular emphasis on integrating LiDAR and camera systems. His major contributions include developing innovative methods for robust LiDAR-camera calibration, such as the INF (Implicit Neural Fusion) framework, which addresses challenges in data representation and extrinsic calibration, and a novel approach using 2D Gaussian splatting to eliminate the need for complex manual target objects. His research on offline and online calibration of mobile robots and SLAM devices has advanced practical navigation systems, enabling easier deployment in real-world environments. With over 30 citations across his most-cited works, Ishikawa's impact is evident in his ability to bridge theoretical challenges with applied solutions. Notably, his work on a quadruped robot platform for selective pesticide spraying demonstrates the real-world application of his robotics expertise in agriculture, while his exploration of category-level articulation estimation using Transformers and 6DoF grasping through reward-consistent learning showcases his versatility in tackling complex perception and manipulation problems.
Research Focus
Key Achievements
Top Papers
- 1INF: Implicit Neural Fusion for LiDAR and Camera11 citations · 2023
- 2Robust LiDAR-Camera Calibration With 2D Gaussian Splatting4 citations · 2025
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- 5Quadruped Robot Platform for Selective Pesticide Spraying2 citations · 2023
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- 8Learning 6DoF Grasping Using Reward-Consistent Demonstration2 citations · 2021