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Authors: Jon Iñiguez De Gordoa ; Javier Barandiaran and Marcos Nieto

Affiliation: Vicomtech Foundation, Basque Research and Technology Alliance (BRTA) Mikeletegi 57, 20009 Donostia-San Sebastián, Spain

Keyword(s): Monocular Depth Estimation, Safe Drone Landing, UAV, Synthetic Dataset, Simulation.

Abstract: As there is a lack of publicly available datasets with depth and surface normal information from a drone’s view, in this paper, we introduce the synthetic and photorealistic AirSimNC dataset. This dataset is used as a benchmark to test the zero-shot cross-dataset performance of monocular depth and safe drone landing area estimation models. We analysed state-of-the-art Deep Learning networks and trained them on the SafeUAV dataset. While the depth models achieved very satisfactory results in the SafeUAV dataset, they showed a scaling error in the AirSimNC benchmark. We also compared the performance of networks trained on the KITTI and NYUv2 datasets, in order to test how training the networks on a bird’s eye view affects in the performance on our benchmark. Regarding the safe landing estimation models, they surprisingly showed barely any zero-shot cross-dataset penalty when it comes to the precision of horizontal surfaces.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Iñiguez De Gordoa, J.; Barandiaran, J. and Nieto, M. (2022). Exploiting AirSim as a Cross-dataset Benchmark for Safe UAV Landing and Monocular Depth Estimation Models. In Proceedings of the 14th International Joint Conference on Computational Intelligence - ROBOVIS; ISBN 978-989-758-611-8; ISSN 2184-3236, SciTePress, pages 454-462. DOI: 10.5220/0011562200003332

@conference{robovis22,
author={Jon {Iñiguez De Gordoa}. and Javier Barandiaran. and Marcos Nieto.},
title={Exploiting AirSim as a Cross-dataset Benchmark for Safe UAV Landing and Monocular Depth Estimation Models},
booktitle={Proceedings of the 14th International Joint Conference on Computational Intelligence - ROBOVIS},
year={2022},
pages={454-462},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011562200003332},
isbn={978-989-758-611-8},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computational Intelligence - ROBOVIS
TI - Exploiting AirSim as a Cross-dataset Benchmark for Safe UAV Landing and Monocular Depth Estimation Models
SN - 978-989-758-611-8
IS - 2184-3236
AU - Iñiguez De Gordoa, J.
AU - Barandiaran, J.
AU - Nieto, M.
PY - 2022
SP - 454
EP - 462
DO - 10.5220/0011562200003332
PB - SciTePress

- Science and Technology Publications, Lda.
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