Papers
5
Total Citations
59
H-Index
3
About
Yiyao Liu is a pioneering researcher at the intersection of robotics, multi-modal perception, and bio-inspired soft robotics. Their work primarily focuses on advancing Simultaneous Localization and Mapping (SLAM) systems that can operate reliably under adverse conditions—such as rain, snow, smoke, and fog—where traditional LiDAR and visual SLAM often fail. Liu’s development of the NTU4DRadLM dataset, a 4D radar-centric multi-modal dataset for localization and mapping (33 citations), has become a foundational resource for robust perception in challenging environments. They have also made significant contributions to sensor fusion through RGBDTCalibNet (11 citations), an end-to-end online extrinsic calibration framework for 3D LiDAR, RGB, and thermal cameras, enabling seamless day-and-night perception. Beyond robotics, Liu’s interdisciplinary work includes an amoeba-inspired magnetic soft robot (11 citations) for integrated tumor/infection therapy and painless postoperative drainage, showcasing their ability to translate robotic principles into biomedical applications. With recent explorations into bioinspired photothermal-mechanochromic composites for aerospace camouflage and anchorless UWB-assisted relative pose estimation for multi-robot systems, Liu continues to push boundaries in both hardware and algorithmic innovation, making their research highly impactful across robotics, healthcare, and defense.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5