# The Visual-Inertial-Dynamical Multirotor Dataset

arXiv: https://arxiv.org/abs/2103.11152 · HTML: https://mlsanity.com/paper/2103.11152/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Mar 2021.

## Built on by (most cited first, 2 of 2)

- [HDVIO2.0: Wind and Disturbance Estimation with Hybrid Dynamics VIO](https://mlsanity.com/paper/2504.00969/) (2025)
- [IMU-Free Body-Frame State Estimation with Sparse Scene Flow for Quadcopters](https://mlsanity.com/paper/2608.20891/) (2026)

## Abstract (arXiv)

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.