Image feature-based real-time RGB-D 3D SLAM with GPU acceleration GPU 가속화를 통한 이미지 특징점 기반 RGB-D 3차원 SLAM

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This paper proposes an image feature-based real-time RGB-D (Red-Green-Blue Depth) 3D SLAM (Simultaneous Localization and Mapping) system. RGB-D data from Kinect style sensors contain a 2D image and per-pixel depth information. 6-DOF (Degree-of-Freedom) visual odometry is obtained through the 3D-RANSAC (RANdom SAmple Consensus) algorithm with 2D image features and depth data. For speed up extraction of features, parallel computation is performed with GPU acceleration. After a feature manager detects a loop closure, a graph-based SLAM algorithm optimizes trajectory of the sensor and builds a 3D point cloud based map.
Publisher
Institute of Control, Robotics and Systems
Issue Date
2013-05
Language
Korean
Citation

Journal of Institute of Control, Robotics and Systems, v.19, no.5, pp.457 - 461

ISSN
1976-5622
URI
http://hdl.handle.net/10203/174414
Appears in Collection
EE-Journal Papers(저널논문)
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