Practical Deep Feature-Based Visual-Inertial Odometry

Charles Hamesse; Charles Hamesse; Michiel Vlaminck; Hiep Luong; Rob Haelterman

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Paper

Practical Deep Feature-Based Visual-Inertial Odometry

Topics: Deep Learning and Neural Networks; Feature Selection and Extraction; Machine Learning Methods; Motion Tracking and Action Recognition; Robotics

In Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods ICPRAM - Volume 1, 240-247, 2024 , Rome, Italy

Authors: Charles Hamesse ^{1

;

2} ; Michiel Vlaminck ² ; Hiep Luong ² and Rob Haelterman ¹

Affiliations: ¹ Department of Mathematics, Royal Military Academy, Belgium ; ² imec, IPI, URC, Ghent University, Belgium

Keyword(s): Visual-Inertial Odometry, Visual Features, Deep Features.

Abstract: We present a hybrid visual-inertial odometry system that relies on a state-of-the-art deep feature matching front-end and a traditional visual-inertial optimization back-end. More precisely, we develop a fully-fledged feature tracker based on the recent SuperPoint and LightGlue neural networks, that can be plugged directly to the estimation back-end of VINS-Mono. By default, this feature tracker returns extremely abundant matches. To bound the computational complexity of the back-end optimization, limiting the number of used matches is desirable. Therefore, we explore various methods to filter the matches while maintaining a high visual-inertial odometry performance. We run extensive tests on the EuRoC machine hall and Vicon room datasets, showing that our system achieves state-of-the-art odometry performance according relative pose errors.

CC BY-NC-ND 4.0

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Paper citation in several formats:

Hamesse, C.; Vlaminck, M.; Luong, H. and Haelterman, R. (2024). Practical Deep Feature-Based Visual-Inertial Odometry. In Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-684-2; ISSN 2184-4313, SciTePress, pages 240-247. DOI: 10.5220/0012320200003654

@conference{icpram24,
author={Charles Hamesse. and Michiel Vlaminck. and Hiep Luong. and Rob Haelterman.},
title={Practical Deep Feature-Based Visual-Inertial Odometry},
booktitle={Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2024},
pages={240-247},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012320200003654},
isbn={978-989-758-684-2},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Practical Deep Feature-Based Visual-Inertial Odometry
SN - 978-989-758-684-2
IS - 2184-4313
AU - Hamesse, C.
AU - Vlaminck, M.
AU - Luong, H.
AU - Haelterman, R.
PY - 2024
SP - 240
EP - 247
DO - 10.5220/0012320200003654
PB - SciTePress