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Authors: Georges Younes 1 ; Daniel Asmar 2 and John Zelek 3

Affiliations: 1 Mechanical Engineering Department, American Univsersity of Beirut, Beirut, Lebanon, Department of Systems Design, University of Waterloo, Waterloo and Canada ; 2 Mechanical Engineering Department, American Univsersity of Beirut, Beirut and Lebanon ; 3 Department of Systems Design, University of Waterloo, Waterloo and Canada

Keyword(s): Feature-based, Direct, Odometry, Localization, Monocular.

Related Ontology Subjects/Areas/Topics: Applications ; Computer Vision, Visualization and Computer Graphics ; Geometry and Modeling ; Image-Based Modeling ; Motion, Tracking and Stereo Vision ; Pattern Recognition ; Robotics ; Software Engineering ; Stereo Vision and Structure from Motion ; Tracking and Visual Navigation

Abstract: Visual Odometry (VO) can be categorized as being either direct (e.g. DSO) or feature-based (e.g. ORB-SLAM). When the system is calibrated photometrically, and images are captured at high rates, direct methods have been shown to outperform feature-based ones in terms of accuracy and processing time; they are also more robust to failure in feature-deprived environments. On the downside, direct methods rely on heuristic motion models to seed an estimate of camera motion between frames; in the event that these models are violated (e.g., erratic motion), direct methods easily fail. This paper proposes FDMO (Feature assisted Direct Monocular Odometry), a system designed to complement the advantages of both direct and featured based techniques to achieve sub-pixel accuracy, robustness in feature deprived environments, resilience to erratic and large inter-frame motions, all while maintaining a low computational cost at frame-rate. Efficiencies are also introduced to decrease the computation al complexity of the feature-based mapping part. FDMO shows an average of 10% reduction in alignment drift, and 12% reduction in rotation drift when compared to the best of both ORB-SLAM and DSO, while achieving significant drift (alignment, rotation & scale) reductions (51%, 61%, 7% respectively) going over the same sequences for a second loop. FDMO is further evaluated on the EuroC dataset and was found to inherit the resilience of feature-based methods to erratic motions, while maintaining the accuracy of direct methods. (More)

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Paper citation in several formats:
Younes, G.; Asmar, D. and Zelek, J. (2019). FDMO: Feature Assisted Direct Monocular Odometry. In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 5: VISAPP; ISBN 978-989-758-354-4; ISSN 2184-4321, SciTePress, pages 737-747. DOI: 10.5220/0007524807370747

@conference{visapp19,
author={Georges Younes. and Daniel Asmar. and John Zelek.},
title={FDMO: Feature Assisted Direct Monocular Odometry},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 5: VISAPP},
year={2019},
pages={737-747},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007524807370747},
isbn={978-989-758-354-4},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 5: VISAPP
TI - FDMO: Feature Assisted Direct Monocular Odometry
SN - 978-989-758-354-4
IS - 2184-4321
AU - Younes, G.
AU - Asmar, D.
AU - Zelek, J.
PY - 2019
SP - 737
EP - 747
DO - 10.5220/0007524807370747
PB - SciTePress