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)