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Erschienen in:
2020 | OriginalPaper | Buchkapitel
Robustifying Direct VO to Large Baseline Motions
verfasst von : Georges Younes, Daniel Asmar, John Zelek
Erschienen in: Computer Vision, Imaging and Computer Graphics Theory and Applications
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Abstract
While Direct Visual Odometry (VO) methods have been shown to outperform feature-based ones in terms of accuracy and processing time, their optimization is sensitive to the initialization pose typically seeded from heuristic motion models. In real-life applications, the motion of a hand-held or head-mounted camera is predominantly erratic, thereby violating the motion models used, causing large baselines between the initializing pose and the actual pose, which in turn negatively impacts the VO performance.
As the camera transitions from a leisure device to a viable sensor, robustifying Direct VO to real-life scenarios becomes of utmost importance. In that pursuit, we propose FDMO, a hybrid VO that makes use of Indirect residuals to seed the Direct pose estimation process. Two variations of FDMO are presented: one that only intervenes when failure in the Direct optimization is detected, and another that performs both Indirect and Direct optimizations on every frame. Various efficiencies are introduced to both the feature detector and the Indirect mapping process, resulting in a computationally efficient approach. Finally, An experimental procedure designed to test the resilience of VO to large baseline motions is used to validate the success of the proposed approach.