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http://dx.doi.org/10.5302/J.ICROS.2013.13.8002

Image Feature-Based Real-Time RGB-D 3D SLAM with GPU Acceleration  

Lee, Donghwa (Dept. of Civil and Environmental Engineering, KAIST)
Kim, Hyongjin (Dept. of Civil and Environmental Engineering, KAIST)
Myung, Hyun (Dept. of Civil and Environmental Engineering, KAIST)
Publication Information
Journal of Institute of Control, Robotics and Systems / v.19, no.5, 2013 , pp. 457-461 More about this Journal
Abstract
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.
Keywords
SLAM; 3D SLAM; RGB-D; image feature;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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