The Visual-Inertial-Dynamical Multirotor Dataset

TL;DR

The VID dataset provides synchronized visual, inertial, and dynamical data, including external forces, for multirotor platforms, enabling advanced pose estimation and external force perception research.

Contribution

This paper introduces the first public dataset combining visual-inertial data with complete dynamical information for multirotor drones in real-world scenarios.

Findings

Contains synchronized imagery, inertial data, and ground truth trajectories.

Includes rotor speed, motor current, control inputs, and ground truth external forces.

Enables evaluation of pose and external force estimation methods.

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 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.