Blackbirdデータセット：高速飛行UAVの知覚評価用大規模データセット
The Blackbird Dataset: A large-scale dataset for UAV perception in aggressive flight
高速で屋内を飛ぶドローンの知覚アルゴリズム評価用に、168回の飛行・17軌道・5環境からなる10時間超の大規模データセットを構築した。
著者: Amado Antonini, Winter Guerra, Varun Murali, Thomas Sayre-McCord, Sertac Karaman
分類: cs.CV, cs.RO
原文アブストラクト
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/
