无人系统与自主计算实验室

Our recent research works are available on Github

Fast-Tracker is a systematic solution that uses an unmanned aerial vehicle (UAV) to aggressively and safely track an agile target. The open-source project releases the tracking algorithm with a simulation of a car-tracking scenario as a demo.

Author: Zhichao Han*, Ruibin Zhang*, Neng Pan*, Chao Xu and Fei Gao from the ZJU Fast Lab

Related Paper: Fast-Tracker: A Robust Aerial System for Tracking AgileTarget in Cluttered Environments, Zhichao Han*, Ruibin Zhang*, Neng Pan*, Chao Xu, Fei Gao, International Conference on Robotics and Automation (ICRA 2021)

Video Linksyoutube or bilibili

Code Linkhttps://github.com/ZJU-FAST-Lab/Fast-tracker


External Forces Resilient Planner is a systematic motion planning framework that can resiliently generate safe trajectories under volatile external forces. This work integrates our previous work, VID-Fusion as the external force estimator.

Author: Yuwei WU and Fei GAO from the ZJU Fast Lab

Related Paper: External Forces Resilient Safe Motion Planning for Quadrotor, Yuwei Wu, Ziming Ding, Chao Xu, Fei Gao, submitted to IEEE Robotics and Automation Letter (RA-L).

Video Linksyoutube or bilibili

Code Link: https://github.com/ZJU-FAST-Lab/forces_resilient_planner


VID-Fusion is a work to estimate odometry and external force simultaneously by a tightly coupled Visual-Inertial-Dynamics state estimator. The open source project releases the code of VID-Fusion, along with an experimental dataset in the real word.

Author: Ziming Ding, Tiankai Yang, Kunyi Zhang, Chao Xu, and Fei Gao from the ZJU FAST Lab.

Related Paper: VID-Fusion: Robust Visual-Inertial-Dynamics Odometry for Accurate External Force Estimation, Ziming Ding, Tiankai Yang, Kunyi Zhang, Chao Xu, and Fei Gao, International Conference on Robotics and Automation (ICRA 2021)

Video Links: Youtube or bilibili

Code Link: https://github.com/ZJU-FAST-Lab/VID-Fusion