Xianjia Yu
Ph.D. Candidate & Researcher
Department of Computing xianjia.yu@utu.fi Office: B6016 ORCID identifier: https://orcid.org/https://orcid.org/0000-0002-9042-3730 |
Robotics, Perception, Machine Learning, Computer Vision, Sensor Fusion, Federated Learning
Xianjia Yu is a doctoral candidate and researcher at the University of Turku in the Department of Computing.
2018 - 2021, a full-time senior robot developer, Deepblue Technology, Shanghai, China.
2016 - 2018, MSc degree in information and communication technology from the University of Turku, Turku, Finland.
2016 - 2018, M.Eng. in Electronics Engineering, Fudan University, Shanghai, P.R. China.
2011 - 2015, bachelor's degree at East China University of Science and Technology, Shanghai, China.
2012 - 2015, Minor in English at East China University of Science and Technology, Shanghai, China.
His research interests are robotics, perception, machine learning, computer vision, sensor fusion, federated learning.
Federated learning-based Active Perception in Multi-robot Systems
Currently involved in three-course teaching:
Hardware Accelerators for Robotics & AI
Robotics and Autonomous Systems
Perception and Navigation in Robotics
- Adaptive Lidar Scan Frame Integration: Tracking Known MAVs in 3D Point Clouds (2021) 2021 20th International Conference on Advanced Robotics (ICAR) Li Qingqing, Yu Xianjia, Peña Queralta Jorge, Westerlund Tomi
(Refereed article in conference proceedings (A4)) - Applications of UWB Networks and Positioning to Autonomous Robots and Industrial Systems (2021) 2021 10th Mediterranean Conference on Embedded Computing (MECO) Yu Xianjia, Li Qingqing, Peña Queralta Jorge, Heikkonen Jukka, Westerlund Tomi
(Refereed article in conference proceedings (A4)) - Cooperative UWB-Based Localization for Outdoors Positioning and Navigation of UAVs aided by Ground Robots (2021) 2021 IEEE International Conference on Autonomous Systems (ICAS) Proceedings Yu Xianjia, Li Qingqing, Peña Queralta Jorge, Heikkonen Jukka, Westerlund Tomi
(Refereed article in conference proceedings (A4)) - Federated Learning in Robotic and Autonomous Systems (2021)
- Procedia Computer Science
(Refereed article in conference proceedings (A4))