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/.