@inproceedings{ecc8cbb79be34e559c13f01f3a4c0e63,
title = "ISS Monocular Depth Estimation Via Vision Transformer",
abstract = "Monocular depth estimation can be used as an energy-efficient and lightweight backup system to the onboard light detection and ranging (LiDAR) instrument. Unfortunately, the monocular depth estimation is an ill-posed problem. Therefore typical methods resort to statistical distributions of image features. In this work, a deep neural network that exploits Vision Transformer in the encoder is trained in a supervised fashion to solve the regression problem. Specifically, the problem considered is monocular depth estimation with images taken at different positions and distances from the International Space Station (ISS) to measure the network performance in a rendezvous maneuver.",
keywords = "Computer vision, Deep learning, Machine learning, Rendezvous, Transformers",
author = "Luca Ghilardi and Andrea Scorsoglio and Roberto Furfaro",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 2nd International Conference on Applied Intelligence and Informatics , AII 2022 ; Conference date: 01-09-2022 Through 03-09-2022",
year = "2023",
doi = "10.1007/978-3-031-25755-1_11",
language = "English (US)",
isbn = "9783031257544",
series = "Studies in Computational Intelligence",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "167--181",
editor = "Cosimo Ieracitano and Nadia Mammone and {Di Clemente}, Marco and Mufti Mahmud and Roberto Furfaro and Morabito, {Francesco Carlo}",
booktitle = "The Use of Artificial Intelligence for Space Applications - Workshop at the 2022 International Conference on Applied Intelligence and Informatics",
address = "Germany",
}