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• W2999815532 abstract "Accurate indoor positioning has attracted a lot of attention for a variety of indoor location-based applications, with the rapid development of mobile devices and their onboard sensors. A hybrid indoor localization method is proposed based on single off-the-shelf smartphone, which takes advantage of its various onboard sensors, including camera, gyroscope and accelerometer. The proposed approach integrates three components: visual-inertial odometry (VIO), point-based area mapping, and plane-based area mapping. A simplified RANSAC strategy is employed in plane matching for the sake of processing time. Since Apple's augmented reality platform ARKit has many powerful high-level APIs on world tracking, plane detection and 3D modeling, a practical smartphone app for indoor localization is developed on an iPhone that can run ARKit. Experimental results demonstrate that our plane-based method can achieve an accuracy of about 0.3 meter, which is based on a much more lightweight model, but achieves more accurate results than the point-based model by directly using ARKit's area mapping. The size of the plane-based model is less than 2KB for a closed-loop corridor area of about 45m*15m, comparing to about 10MB of the point-based model." @default.
• W2999815532 created "2020-01-23" @default.
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• W2999815532 date "2019-10-01" @default.
• W2999815532 modified "2023-10-15" @default.
• W2999815532 title "Multi-level Scene Modeling and Matching for Smartphone-Based Indoor Localization" @default.
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• W2999815532 doi "https://doi.org/10.1109/ismar-adjunct.2019.00-22" @default.
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