Evaluating the Impact of Head Motion on Monocular Visual Odometry with Synthetic Data

Charles Hamesse, Charles Hamesse, Hiep Luong, Rob Haelterman

2022

Abstract

Monocular visual odometry is a core component of visual Simultaneous Localization and Mapping (SLAM). Nowadays, headsets with a forward-pointing camera abound for a wide range of use cases such as extreme sports, firefighting or military interventions. Many of these headsets do not feature additional sensors such as a stereo camera or an IMU, thus evaluating the accuracy and robustness of monocular odometry remains critical. In this paper, we develop a novel framework for procedural synthetic dataset generation and a dedicated motion model for headset-mounted cameras. With our method, we study the performance of the leading classes of monocular visual odometry algorithms, namely feature-based, direct and deep learning-based methods. Our experiments lead to the following conclusions: i) the performance deterioration on headset-mounted camera images is mostly caused by head rotations and not by translations caused by human walking style, ii) feature-based methods are more robust to fast head rotations compared to direct and deep learning-based methods, and iii) it is crucial to develop uncertainty metrics for deep learning-based odometry algorithms.

Paper Citation

in Harvard Style

Hamesse C., Luong H. and Haelterman R. (2022). Evaluating the Impact of Head Motion on Monocular Visual Odometry with Synthetic Data. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP; ISBN 978-989-758-555-5, SciTePress, pages 836-843. DOI: 10.5220/0010881500003124

in Bibtex Style

@conference{visapp22,
author={Charles Hamesse and Hiep Luong and Rob Haelterman},
title={Evaluating the Impact of Head Motion on Monocular Visual Odometry with Synthetic Data},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP},
year={2022},
pages={836-843},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010881500003124},
isbn={978-989-758-555-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP
TI - Evaluating the Impact of Head Motion on Monocular Visual Odometry with Synthetic Data
SN - 978-989-758-555-5
AU - Hamesse C.
AU - Luong H.
AU - Haelterman R.
PY - 2022
SP - 836
EP - 843
DO - 10.5220/0010881500003124
PB - SciTePress