Open AccessApplication of Hybrid Monocular SLAM Method in Augmented Reality
Author(s) -
Zuoming Zhang,
Hanwen Wang,
Man Shu,
Xin Wang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1748/4/042010
Subject(s) - bundle adjustment , artificial intelligence , monocular , computer vision , augmented reality , feature (linguistics) , computer science , simultaneous localization and mapping , point cloud , monocular vision , process (computing) , structure from motion , point (geometry) , motion (physics) , mathematics , image (mathematics) , mobile robot , robot , geometry , philosophy , linguistics , operating system
In this paper, we design a hybrid (semi-direct) approach to simultaneous localization and mapping (SLAM) for monocular cameras and apply it to augmented reality (AR) for monocular cameras. We combine the advantagesof the direct method and the feature point method. We use both photometric bundle adjustment which is robust to camera exposure time and motion bundle adjustment which is geometrically robust based on feature points to do tracking process. This approach can maintain an intuitive direct local map as well as a reusable global sparse feature point map. Through the processing of point clouds, such as PCA plane detection and grid reconstruction, we greatly improve the effect of the augmented reality system.