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/.

中文翻译：

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黑鸟无人机数据集

本文介绍了 Blackbird 无人机 (UAV) 数据集，这是一套大型传感器数据和来自定制四旋翼平台的相应地面实况，该平台配备惯性测量单元 (IMU)、转子转速计和虚拟颜色、灰度和深度相机。由于对飞行器敏捷、自主操作的需求不断增加，该数据集旨在促进高性能无人机感知算法的开发和评估。该数据集包含来自我们的四旋翼飞行器超过 10 小时的数据，通过 5 种不同的视觉环境以不同的最大速度（0.5 到 13.8 ms-1）追踪 18 条不同的轨迹，总共进行了 176 次独特的飞行。对于每次飞行，我们提供 120 Hz 灰度、60 Hz RGB-D、除了 100 Hz IMU、~190 Hz 电机速度传感器和 360 Hz 毫米级精确运动捕捉地面实况之外，还有来自前向立体和向下的真实感虚拟相机的 60 Hz 语义分割图像。因此，Blackbird UAV 数据集非常适合开发用于视觉惯性导航、3D 重建和深度估计的算法。作为未来算法的基准，报告了两种最先进的视觉里程计算法的性能，并且数据集中包含用于与基准进行比较的脚本。该数据集可从 http://blackbird-dataset.mit.edu/ 下载。因此，Blackbird UAV 数据集非常适合开发用于视觉惯性导航、3D 重建和深度估计的算法。作为未来算法的基准，报告了两种最先进的视觉里程计算法的性能，并且数据集中包含用于与基准进行比较的脚本。该数据集可从 http://blackbird-dataset.mit.edu/ 下载。因此，Blackbird UAV 数据集非常适合开发用于视觉惯性导航、3D 重建和深度估计的算法。作为未来算法的基准，报告了两种最先进的视觉里程计算法的性能，并且数据集中包含用于与基准进行比较的脚本。该数据集可从 http://blackbird-dataset.mit.edu/ 下载。