ALTO: UAVの視覚的場所認識と自己位置推定のための大規模データセット
ALTO: A Large-Scale Dataset for UAV Visual Place Recognition and Localization
ヘリコプターでオハイオ州とペンシルベニア州を飛行した約150kmと260kmの長距離軌道からなる、UAVの視覚的場所認識・自己位置推定研究用の大規模データセットを公開した。
著者: Ivan Cisneros, Peng Yin, Ji Zhang, Howie Choset, Sebastian Scherer
分類: cs.CV, cs.RO
原文アブストラクト
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.
