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.
@article{arxiv.2207.12317,
title = {ALTO: A Large-Scale Dataset for UAV Visual Place Recognition and Localization},
author = {Ivan Cisneros and Peng Yin and Ji Zhang and Howie Choset and Sebastian Scherer},
journal= {arXiv preprint arXiv:2207.12317},
year = {2022}
}