Scholar
Fangcheng Zhu
Google Scholar ID: WYlGZZUAAAAJ
PhD Candidate at HKU
Citations & Impact
Citations
555
H-index
12
i10-index
13
Publications
20
Co-authors
0
Publications
Resume (English only)
Academic Achievements
- 2023 Best Overall and Best Student Paper Finalist in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); 2023 Best Paper Award on Robot Mechanisms and Design in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); 2023 Best Paper Finalist (Navigation) in IEEE International Conference on Robotics and Automation (ICRA); 2021-2025 Postgraduate Scholarship of The University of Hong Kong (HKU); 2021 Outstanding Graduate Honor of Harbin Institute of Technology; 2020 Meritorious Winner of Mathematical Contest in Modeling; 2019 Outstanding Student of Harbin Institute of Technology; 2019 Second Prize Scholarship of Harbin Institute of Technology (Shenzhen); 2018 Outstanding Student Leader of Harbin Institute of Technology; 2017 Outstanding Communist Youth League Cadre of Harbin Institute of Technology; 2017 First Class Scholarship of Harbin Institute of Technology (Shenzhen).
Research Experience
- Conference Presentation: 'Swarm-LIO: Decentralized Swarm LiDAR-inertial Odometry' at ICRA 2023, London, UK; Conference Presentation: 'Robust Real-time LiDAR-inertial Initialization' at IROS 2023, Kyoto, Japan; Academic Talk: 'Swarm-LIO: Decentralized Swarm LiDAR-inertial Odometry' at School of Artificial Intelligence, Sun Yat-sen University, Zhuhai, May 2023.
Education
- Ph.D. student at MaRS Lab, Department of Mechanical Engineering, University of Hong Kong (HKU), 2021-2025 expected, supervised by Prof. Fu Zhang; Bachelor's Degree in Automation from Harbin Institute of Technology (HIT), Shenzhen, 2021, undergraduate thesis: 'Simultaneously Localization and Mapping Method based on Solid-State LiDAR', supervised by Prof. Jie Mei.
Background
- Research interests include but are not limited to Robotics, LiDAR-based SLAM, Sensor Calibration and Fusion, Aerial Swarm Systems, Quadrotor Autonomous Navigation. Dedicated to developing stable and feasible algorithms that enable robots (UAVs, service robots, autonomous vehicles) to perceive the external environment comprehensively and precisely. Envision a future where robots can truly enter every household and enhance the quality of human life.
Miscellany
- Actively contributes to open-source projects. Research code on GitHub has accumulated over 5k stars. Notable repositories include LiDAR_IMU_Init (★868), Swarm-LIO2 (★231), FAST-LIO (★2.8k), and FAST-LIVO2 (★1.2k).
Co-authors