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Sensors 2019, 19(2), 302; https://doi.org/10.3390/s19020302

SDVL: Efficient and Accurate Semi-Direct Visual Localization

RoboticsLab-URJC, Universidad Rey Juan Carlos, Fuenlabrada, 28943 Madrid, Spain
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Received: 21 November 2018 / Revised: 7 January 2019 / Accepted: 10 January 2019 / Published: 14 January 2019
(This article belongs to the Section Physical Sensors)
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Abstract

Visual 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
Keywords: Monocular Vision; SLAM Monocular Vision; SLAM
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Perdices, E.; Cañas, J.M. SDVL: Efficient and Accurate Semi-Direct Visual Localization. Sensors 2019, 19, 302.

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