SDVL: Efficient and Accurate Semi-Direct Visual Localization.

Autor: Perdices E; RoboticsLab-URJC, Universidad Rey Juan Carlos, Fuenlabrada, 28943 Madrid, Spain. eperdices@gsyc.urjc.es., Cañas JM; RoboticsLab-URJC, Universidad Rey Juan Carlos, Fuenlabrada, 28943 Madrid, Spain. josemaria.plaza@urjc.es.
Jazyk: angličtina
Zdroj: Sensors (Basel, Switzerland) [Sensors (Basel)] 2019 Jan 14; Vol. 19 (2). Date of Electronic Publication: 2019 Jan 14.
DOI: 10.3390/s19020302
Abstrakt: 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.
Databáze: MEDLINE
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