2018/10/03 by Amado Antonini, Winter Guerra, Antonini, Amado +7 · 1 citation
Computer Science · Engineering · #Advanced Vision and Imaging #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Robotics (cs.RO) #Robotics and Sensor-Based Localization #Video Surveillance and Tracking Methods
paper · pdf · doi:10.48550/arxiv.1810.01987
openalex publication_date 2018/10/03 · openalex created_date 2022/08/02 · openalex updated_date 2026/07/28
The Blackbird unmanned aerial vehicle (UAV) dataset is a large-scale,\naggressive indoor flight dataset collected using a custom-built quadrotor\nplatform for use in evaluation of agile perception.Inspired by the potential of\nfuture high-speed fully-autonomous drone racing, the Blackbird dataset contains\nover 10 hours of flight data from 168 flights over 17 flight trajectories and 5\nenvironments at velocities up to 7.0ms-1. Each flight includes sensor data\nfrom 120Hz stereo and downward-facing photorealistic virtual cameras, 100Hz\nIMU, \∼190Hz motor speed sensors, and 360Hz millimeter-accurate motion\ncapture ground truth. Camera images for each flight were photorealistically\nrendered using FlightGoggles across a variety of environments to facilitate\neasy experimentation of high performance perception algorithms. The dataset is\navailable for download at http://blackbird-dataset.mit.edu/\n