Papers
8
Total Citations
497
H-Index
5
About
Kewei Hu is at the forefront of agricultural robotics, pioneering the integration of 3D vision, perception, and autonomous navigation to transform precision horticulture. His research centers on developing intelligent robotic systems capable of operating in unstructured field environments, with a particular focus on automated fruit harvesting and in-situ plant phenotyping. Hu’s most impactful work includes a self-developed structural crack recognition robot (267 citations) and a geometry-aware 3D point cloud learning method for precise cutting-point detection in lychee harvesting (90 citations). He has also made significant contributions to sensor calibration with an observability-aware LiDAR-IMU calibration method (76 citations) and developed the Fast Fruit 3D Detector (FF3D), a rapid and accurate framework for robotic harvesting. His context-aware navigation and semantic mapping systems enable robots to autonomously explore and model complex horticultural environments. Hu’s work on Phenobot, an autodigital modeling system for in-situ phenotyping, further underscores his commitment to bridging robotics and sustainable agriculture. With over 500 total citations, Kewei Hu is shaping the future of intelligent agricultural automation.
Research Focus
Key Achievements
Top Papers
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- 3Observability-Aware Intrinsic and Extrinsic Calibration of LiDAR-IMU Systems76 citations · 2022
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- 5FF3D: A Rapid and Accurate 3D Fruit Detector for Robotic Harvesting15 citations · 2024
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