A data set of aerial imagery from robotics simulator for map-based localization systems benchmark
Rokas Jurevi\v{c}ius, Virginijus Marcinkevi\v{c}ius

TL;DR
This paper introduces AIR, a large public dataset of aerial imagery from a robotics simulator, designed to benchmark map-based localization, visual odometry, and SLAM algorithms for high-altitude UAV flights.
Contribution
It provides the largest publicly available aerial imagery dataset from a robotics simulator for high-altitude UAV localization research.
Findings
Largest available public dataset with downward facing camera imagery
Contains over 100,000 images from urban and forest environments
Supports development and benchmarking of high-altitude UAV localization algorithms
Abstract
Purpose: This paper presents a new dataset of Aerial Imagery from Robotics simulator (abbr. AIR). AIR dataset aims to provide a starting point for localization system development and to become a typical benchmark for accuracy comparison of map-based localization algorithms, visual odometry, and SLAM for high altitude flights. Design/methodology/approach: The presented dataset contains over 100 thousand aerial images captured from Gazebo robotics simulator using orthophoto maps as a ground plane. Flights with 3 different trajectories are performed on maps from urban and forest environment at different altitudes, totaling over 33 kilometers of flight distance. Findings: The review of previous researches shows, that the presented dataset is the largest currently available public dataset with downward facing camera imagery. Originality/value: This paper presents the problem of missing…
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