The Blackbird Dataset: A large-scale dataset for UAV perception in aggressive flight

TL;DR

The Blackbird dataset provides extensive high-speed UAV flight data with synchronized sensors and photorealistic images, supporting research in agile perception for autonomous drone racing.

Contribution

It introduces a large-scale, high-velocity UAV dataset with diverse environments, photorealistic rendering, and comprehensive sensor data for evaluating agile perception algorithms.

Findings

Over 10 hours of flight data collected at speeds up to 7 m/s.

Includes synchronized stereo, IMU, motor speed sensors, and motion capture ground truth.

Photorealistic rendered images for diverse environments.

Abstract

The Blackbird unmanned aerial vehicle (UAV) dataset is a large-scale, aggressive indoor flight dataset collected using a custom-built quadrotor platform for use in evaluation of agile perception.Inspired by the potential of future high-speed fully-autonomous drone racing, the Blackbird dataset contains over 10 hours of flight data from 168 flights over 17 flight trajectories and 5 environments at velocities up to $7.0ms^-1$. Each flight includes sensor data from 120Hz stereo and downward-facing photorealistic virtual cameras, 100Hz IMU, $\sim190Hz$ motor speed sensors, and 360Hz millimeter-accurate motion capture ground truth. Camera images for each flight were photorealistically rendered using FlightGoggles across a variety of environments to facilitate easy experimentation of high performance perception algorithms. The dataset is available for download at http://blackbird-dataset.mit.edu/