SDVL: Efficient and Accurate Semi-Direct Visual Localization
AbstractVisual Simultaneous Localization and Mapping (SLAM) approaches have achieved a major breakthrough in recent years. This paper presents a new monocular visual odometry algorithm able to localize in 3D a robot or a camera inside an unknown environment in real time, even on slow processors such as those used in unmanned aerial vehicles (UAVs) or cell phones. The so-called semi-direct visual localization (SDVL) approach is focused on localization accuracy and uses semi-direct methods to increase feature-matching efficiency. It uses inverse-depth 3D point parameterization. The tracking thread includes a motion model, direct image alignment, and optimized feature matching. Additionally, an outlier rejection mechanism (ORM) has been implemented to rule out misplaced features, improving accuracy especially in partially dynamic environments. A relocalization module is also included but keeping the real-time operation. The mapping thread performs an automatic map initialization with homography, a sampled integration of new points and a selective map optimization. The proposed algorithm was experimentally tested with international datasets and compared to state-of-the-art algorithms. View Full-Text
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Perdices, E.; Cañas, J.M. SDVL: Efficient and Accurate Semi-Direct Visual Localization. Sensors 2019, 19, 302.
Perdices E, Cañas JM. SDVL: Efficient and Accurate Semi-Direct Visual Localization. Sensors. 2019; 19(2):302.Chicago/Turabian Style
Perdices, Eduardo; Cañas, José M. 2019. "SDVL: Efficient and Accurate Semi-Direct Visual Localization." Sensors 19, no. 2: 302.
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