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Author: Alexander K. Seewald

Affiliation: Seewald Solutions, Lärchenstraße 1, A-4616 Weißkirchen a.d. Traun, Austria

Keyword(s): Deep Learning, Obstacle Avoidance, Autonomous Robotics, Mobile Robotics, View Disparity, Stereo View Algorithms.

Abstract: Obstacle avoidance is an essential feature for autonomous robots. Recently, stereo view algorithm challenges have started to focus on fast algorithms with low computational expense, which may enable obstacle avoidance on mobile robots using only stereo cameras. Therefore we have evaluated classical and state-of-the-art stereo salgorithms qualitatively using internal datasets from Seewald (2020), showing that – although improvements are discernable – current algorithms still fail at this task. As it is known (e.g. from Muller et al. (2004)) that deep learning networks trained on stereo views do not rely on view disparity – confirmed by the fact that networks perform almost equally well when trained with only one camera image – we present an alternative network which is end-to-end trained on a simple layer of biologically plausible disparity-sensitive cells and show that it performs equally well as systems trained on raw image data, but must by design rely on view disparity alone.

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Paper citation in several formats:
Seewald, A. (2022). Evaluating Two Ways for Mobile Robot Obstacle Avoidance with Stereo Cameras: Stereo View Algorithms and End-to-End Trained Disparity-sensitive Networks. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-547-0; ISSN 2184-433X, SciTePress, pages 663-672. DOI: 10.5220/0010878500003116

@conference{icaart22,
author={Alexander K. Seewald.},
title={Evaluating Two Ways for Mobile Robot Obstacle Avoidance with Stereo Cameras: Stereo View Algorithms and End-to-End Trained Disparity-sensitive Networks},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2022},
pages={663-672},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010878500003116},
isbn={978-989-758-547-0},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Evaluating Two Ways for Mobile Robot Obstacle Avoidance with Stereo Cameras: Stereo View Algorithms and End-to-End Trained Disparity-sensitive Networks
SN - 978-989-758-547-0
IS - 2184-433X
AU - Seewald, A.
PY - 2022
SP - 663
EP - 672
DO - 10.5220/0010878500003116
PB - SciTePress

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