ALTO: A Large-Scale Dataset for UAV Visual Place Recognition and Localization
This dataset addresses the need for benchmarking and development of visual place recognition and localization methods for unmanned aerial vehicles, providing a resource for researchers in robotics and computer vision.
The authors introduced the ALTO dataset, a large-scale vision-focused dataset for UAV visual place recognition and localization, comprising two long trajectories (approximately 150km and 260km) with high-precision ground truth data and reference imagery, which they claim is the largest real-world aerial-vehicle dataset of its kind.
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.