The Blackbird UAV dataset

Abstract

This article describes the Blackbird unmanned aerial vehicle (UAV) Dataset, a large-scale suite of sensor data and corresponding ground truth from a custom-built quadrotor platform equipped with an inertial measurement unit (IMU), rotor tachometers, and virtual color, grayscale, and depth cameras. Motivated by the increasing demand for agile, autonomous operation of aerial vehicles, this dataset is designed to facilitate the development and evaluation of high-performance UAV perception algorithms. The dataset contains over 10 hours of data from our quadrotor tracing 18 different trajectories at varying maximum speeds (0.5 to 13.8 ms-1) through 5 different visual environments for a total of 176 unique flights. For each flight, we provide 120 Hz grayscale, 60 Hz RGB-D, and 60 Hz semantically segmented images from forward stereo and downward-facing photorealistic virtual cameras in addition to 100 Hz IMU, ~190 Hz motor speed sensors, and 360 Hz millimeter-accurate motion capture ground truth. The Blackbird UAV dataset is therefore well suited to the development of algorithms for visual inertial navigation, 3D reconstruction, and depth estimation. As a benchmark for future algorithms, the performance of two state-of-the-art visual odometry algorithms are reported and scripts for comparing against the benchmarks are included with the dataset. The dataset is available for download at http://blackbird-dataset.mit.edu/ .

The MADMAX data set for visual‐inertial rover navigation on Mars

“…In addition, the results from the state of the art can be used as a baseline for other navigation algorithms. Our approach is similar to that of Antonini et al (2020), however in our case targeted at planetary robotics instead of indoor unmanned aerial vehicle operation. To the best of our knowledge, no such evaluation and publicly available state‐of‐the‐art performance reference for navigation is available for planetary rover navigation yet.…”

The Visual-Inertial-Dynamical Multirotor Dataset

“…As the demand of GPS-denied navigation increases, several datasets [1]- [5] for state estimation of aerial robots have been proposed. These datasets typically provide exteroceptive sensing, including imagery and range measurements, and proprioceptive data such as IMUs, for developing onboard state estimation algorithms.…”

“…This dataset consists of visual, event, and inertial streams collected in aggressive flights. Besides, the Blackbird UAV dataset [5] also targets high-speed applications. It provides fruitful scenes with photorealistic image rendering and also includes rotor tachometers.…”

“…Nevertheless, almost no dataset focuses on drone dynamical characteristics to support the researches mentioned above. The Blackbird dataset [5] takes one step forward towards this by providing additional rotor tachometers. However, the dynamical model between rotor tachometers and propeller thrust cannot wholly describe a multirotor's motion as a rigid body and cannot accurately estimate the external force and moment in real situations.…”

“…Moreover, the Blackbird dataset provides the desired speed of motors and synthetic visual measurements, which both deviate from actual flights' status. Besides, even in simulation environments, Blackbird dataset [5] does not contain ground truth data for external forces.…”