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Authors: Andrejs Zujevs and Agris Nikitenko

Affiliation: Faculty of Computer Science and Information Technology, Riga Technical University, Latvia

Keyword(s): Datasets, Event-based Vision, Neuromorphic Vision, Visual Navigation, Concise Review.

Abstract: Visual navigation is becoming the primary approach to the way unmanned vehicles such as mobile robots and drones navigate in their operational environment. A novel type of visual sensor named dynamic visual sensor or event-based camera has significant advantages over conventional digital colour or grey-scale cameras. It is an asynchronous sensor with high temporal resolution and high dynamic range. Thus, it is particularly promising for the visual navigation of mobile robots and drones. Due to the novelty of this sensor, publicly available datasets are scarce. In this paper, a total of nine datasets aimed at event-based visual navigation are reviewed and their most important properties and features are pointed out. Major aspects for choosing an appropriate dataset for visual navigation tasks are also discussed.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Zujevs, A. and Nikitenko, A. (2021). Visual Navigation Datasets for Event-based Vision: 2014-2021. In Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics - ICINCO; ISBN 978-989-758-522-7; ISSN 2184-2809, SciTePress, pages 507-513. DOI: 10.5220/0010607105070513

@conference{icinco21,
author={Andrejs Zujevs. and Agris Nikitenko.},
title={Visual Navigation Datasets for Event-based Vision: 2014-2021},
booktitle={Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics - ICINCO},
year={2021},
pages={507-513},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010607105070513},
isbn={978-989-758-522-7},
issn={2184-2809},
}

TY - CONF

JO - Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics - ICINCO
TI - Visual Navigation Datasets for Event-based Vision: 2014-2021
SN - 978-989-758-522-7
IS - 2184-2809
AU - Zujevs, A.
AU - Nikitenko, A.
PY - 2021
SP - 507
EP - 513
DO - 10.5220/0010607105070513
PB - SciTePress

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