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School/Department:Insititute of Unmanned System
Professional Title:Associate Professor
Degree:哲学博士学位
Associate Professor
Date of Employment:2024-10-17
School/Department:Insititute of Unmanned System
Education Level:博士研究生
Contact Information:xuhao3e8 AT buaa DOT edu DOT cn
Degree:哲学博士学位
Status:Employed
Alma Mater:Hong Kong University of Science and Technology
Dr. Xu Hao is currently an associate professor at the Institute of Unmanned Systems, Beihang University (BUAA). He received his Ph.D. from the Department of Electronic and Computer Engineering at the Hong Kong University of Science and Technology, where he was supervised by Prof. Shaojie Shen. He later worked as a multi-sensor information fusion algorithm engineer at DJI. His research interests include 1) state estimation for drone swarms; 2) cooperative and distributed SLAM (CSLAM); and 3) autonomous drone swarm systems. Dr. Xu has published papers in top robotics journals and conferences such as IEEE Transactions on Robotics, IEEE Robotics and Automation Letters, and ICRA, IROS, and serves as a reviewer for these venues. Dr. Xu Hao was awarded the Best Paper Award by the top international robotics journal, IEEE Transactions on Robotics. This was the first time the award was given to a team from China (including Hong Kong, Macau, and Taiwan). He has also released several open-source algorithms and software, including D2SLAM, Omni-Swarm, VINS-Fisheye, and FoxTracker.
[1] 2018.2-2023.1
Hong Kong University of Science and Technology | Electronic and Computer Engineering (ECE) | Postgraduate (Doctoral) | Doctoral degree
[2] 2012.9-2016.6
University of Science and Technology of China | Physics | Bachelor | Bachelor's Degree in Science
[1] 2024.10-Now
Insititute of unmanned system |
Beihang University
| associate professor
| 现任
[2] 2023.3-2024.9
Flight System |
DJI
| engineer
| During his time at DJI, Dr. Xu developed the SLAM 2/3 localization algorithm, which was implemented in the DJI Air3S drone. This algorithm enables the drone to intelligently return to home and avoid obstacles when transitioning between indoor and outdoor.
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