The Visual-Inertial-Dynamical Multirotor Dataset
It provides a new dataset for robotics researchers working on aerial systems requiring force perception, but it is incremental as it extends existing datasets with additional dynamical information.
This paper introduces the Visual-Inertial-Dynamical (VID) dataset, which addresses the lack of datasets for external force perception in applications like aerial transportation by providing synchronized visual-inertial data, ground truth trajectories, and dynamical measurements such as rotor speeds and 6-axis force data, enabling evaluation of pose and force estimation.
Recently, the community has witnessed numerous datasets built for developing and testing state estimators. However, for some applications such as aerial transportation or search-and-rescue, the contact force or other disturbance must be perceived for robust planning and control, which is beyond the capacity of these datasets. This paper introduces a Visual-Inertial-Dynamical (VID) dataset, not only focusing on traditional six degrees of freedom (6-DOF) pose estimation but also providing dynamical characteristics of the flight platform for external force perception or dynamics-aided estimation. The VID dataset contains hardware synchronized imagery and inertial measurements, with accurate ground truth trajectories for evaluating common visual-inertial estimators. Moreover, the proposed dataset highlights rotor speed and motor current measurements, control inputs, and ground truth 6-axis force data to evaluate external force estimation. To the best of our knowledge, the proposed VID dataset is the first public dataset containing visual-inertial and complete dynamical information in the real world for pose and external force evaluation. The dataset: https://github.com/ZJU-FAST-Lab/VID-Dataset and related files: https://github.com/ZJU-FAST-Lab/VID-Flight-Platform are open-sourced.