The Visual-Inertial-Dynamical Multirotor Dataset

Abstract

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 robust 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 (6DOF) pose estimation but also providing dynamical characteristics of the flight platform for external force perception or dynamics-aided estimation. The VID dataset contains hard synchronized imagery and inertial measurements, with accurate ground truth trajectories for evaluating common visual-inertial estimators. Moreover, the proposed dataset highlights the measurements of rotor speed and motor current, dynamical 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 for pose and external force evaluation.

DIDO: Deep Inertial Quadrotor Dynamical Odometry

“…There are only the Blackbird dataset [21] and the VID dataset [22] in the flying robot community that contain quadrotor dynamics. The former trajectories are too repetitive to generalize well for networks, while the latter suffers from insufficient data for training.…”