A data set of aerial imagery from robotics simulator for map-based localization systems benchmark

Abstract

Purpose

The purpose of this paper is to present a new data set of aerial imagery from robotics simulator (AIR). AIR data set 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 data set contains over 100,000 aerial images captured from Gazebo robotics simulator using orthophoto maps as a ground plane. Flights with three 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 research studies show that the presented data set is the largest currently available public data set with downward facing camera imagery.

Originality/value

This paper presents the problem of missing publicly available data sets for high-altitude (100‒3,000 meters) UAV flights; the current state-of-the-art research studies performed to develop map-based localization system for UAVs depend on real-life test flights and custom-simulated data sets for accuracy evaluation of the algorithms. The presented new data set solves this problem and aims to help the researchers to improve and benchmark new algorithms for high-altitude flights.

Citation

Jurevičius, R. and Marcinkevičius, V. (2020), "A data set of aerial imagery from robotics simulator for map-based localization systems benchmark", International Journal of Intelligent Unmanned Systems, Vol. 8 No. 3, pp. 177-186. https://doi.org/10.1108/IJIUS-12-2018-0035