机构:
Keio Univ, Grad Sch Sci & Technol, Tokyo 108, JapanKeio Univ, Grad Sch Sci & Technol, Tokyo 108, Japan
Hirose, Keisuke
[1
]
Saito, Hideo
论文数: 0引用数: 0
h-index: 0
机构:
Keio Univ, Grad Sch Sci & Technol, Tokyo 108, JapanKeio Univ, Grad Sch Sci & Technol, Tokyo 108, Japan
Saito, Hideo
[1
]
机构:
[1] Keio Univ, Grad Sch Sci & Technol, Tokyo 108, Japan
来源:
PROCEEDINGS OF THE BRITISH MACHINE VISION CONFERENCE 2012
|
2012年
关键词:
D O I:
10.5244/C.26.83
中图分类号:
TP18 [人工智能理论];
学科分类号:
081104 ;
0812 ;
0835 ;
1405 ;
摘要:
Simultaneous localization and mapping (SLAM) is a technique to simultaneously perform mapping of environments and localization of a camera in real-time. Most existing monocular vision based SLAM techniques use point features as landmarks. However, images of artificial environments with little texture often contain many line segments, whereas few point features can be localized in such a scene. We propose here a real-time line-based SLAM system, and a novel method for describing the features of line segments (LEHF: Line-based Eight-directional Histogram Feature) in order to establish correct 2D and 3D line correspondences (2D-3D correspondences). LEHF is a fast and efficient way of describing features of line segments, which are detected by the line segment detector (LSD) method. The line-based orthogonal iteration (LBOI) method and the RANSAC algorithm are applied for the camera pose estimation. We conducted an experiment in order to test our SLAM system in a desktop environment and to perform augmented reality (AR). Moreover our SLAM system was evaluated by synthetic data.
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页数:11
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[21]
Zhang X., 2009, INT S COMPUTER NETWO, P1, DOI [DOI 10.1109/EBISS.2009.5138081, DOI 10.1109/IEDM.2009.5424420]