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Visual SLAM for Flying Vehicles
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BibTeX
@MISC{Steder_visualslam,
author = {Bastian Steder and Giorgio Grisetti and Cyrill Stachniss and Wolfram Burgard},
title = {Visual SLAM for Flying Vehicles},
year = {}
}
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Abstract
Abstract — The ability to learn a map of the environment is important for numerous types of robotic vehicles. In this paper, we address the problem of learning a visual map of the ground using flying vehicles. We assume that the vehicles are equipped with one or two cheap downlooking cameras in combination with an attitude sensor. Our approach is able to construct a visual map that can later on be used for navigation. Key advantages of our approach are that it is comparably easy to implement, that it can robustly deal with noisy camera images, and that it can operate either with a monocular camera or a stereo camera system. Our technique uses visual features and estimates the correspondences between features using a variant of the PROSAC algorithm. This allows our approach to extract spatial constraints between camera poses which can then be used to address the SLAM problem by applying graph methods. Furthermore, we address the problem of efficiently identifying loop closures. We performed several experiments with flying vehicles which demonstrate that our method is able to construct maps of large outdoor and indoor environments. Index Terms — SLAM, vision, flying vehicles, attitude sensor I.
Keyphrases
visual slam visual map key advantage loop closure numerous type graph method attitude sensor indoor environment large outdoor index term slam spatial constraint monocular camera noisy camera image slam problem prosac algorithm visual feature camera pose robotic vehicle stereo camera system several experiment
