论文标题
Alto：用于无人机视觉位置识别和本地化的大型数据集
ALTO: A Large-Scale Dataset for UAV Visual Place Recognition and Localization
论文作者
论文摘要
我们介绍了Alto数据集，该数据集是一种以视觉为中心的数据集，用于开发和基准为无人机的视觉位置识别和本地化方法的开发和基准测试。该数据集由两架由直升机在俄亥俄州和宾夕法尼亚州飞行的长度（约150公里和260公里）的轨迹组成，其中包括高精度的GPS gps-Ins地面真相位置数据，高精度加速度计读数，激光器高度读数以及RGB向下面对面的摄像头像。此外，我们还提供了飞行路径上的参考图像，这使得该数据集适用于VPR基准测试和本地化中常见的其他任务，例如图像注册和视觉探光。据作者所知，这是此类最大的现实世界空中车辆数据集。我们的数据集可从https://github.com/metaslam/alto获得。
We present the ALTO dataset, a vision-focused dataset for the development and benchmarking of Visual Place Recognition and Localization methods for Unmanned Aerial Vehicles. The dataset is composed of two long (approximately 150km and 260km) trajectories flown by a helicopter over Ohio and Pennsylvania, and it includes high precision GPS-INS ground truth location data, high precision accelerometer readings, laser altimeter readings, and RGB downward facing camera imagery. In addition, we provide reference imagery over the flight paths, which makes this dataset suitable for VPR benchmarking and other tasks common in Localization, such as image registration and visual odometry. To the author's knowledge, this is the largest real-world aerial-vehicle dataset of this kind. Our dataset is available at https://github.com/MetaSLAM/ALTO.
