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 . Each flight includes sensor data from 120Hz stereo and downward-facing photorealistic virtual cameras, 100Hz IMU, 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…
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Taxonomy
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
