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Ruidong Zhang and Xinguang Zhang
When using deep learning networks for dynamic feature rejection in SLAM systems, problems such as a priori static object motion leading to disturbed build quality and accuracy and slow system runtime are prone to occur. In this paper, based on the ORB-SL...
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Sheng Miao, Xiaoxiong Liu, Dazheng Wei and Changze Li
A visual localization approach for dynamic objects based on hybrid semantic-geometry information is presented. Due to the interference of moving objects in the real environment, the traditional simultaneous localization and mapping (SLAM) system can be c...
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Longyu Zhang, Hao Xia, Qingjun Liu, Chunyang Wei, Dong Fu and Yanyou Qiao
Positioning information has become one of the most important information for processing and displaying on smart mobile devices. In this paper, we propose a visual positioning method using RGB-D image on smart mobile devices. Firstly, the pose of each ima...
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Giulia Marchesi, Christian Eichhorn, David A. Plecher, Yuta Itoh and Gudrun Klinker
Augmented Reality (AR) has increasingly benefited from the use of Simultaneous Localization and Mapping (SLAM) systems. This technology has enabled developers to create AR markerless applications, but lack semantic understanding of their environment. The...
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Junhao Cheng, Zhi Wang, Hongyan Zhou, Li Li and Jian Yao
Most Simultaneous Localization and Mapping (SLAM) methods assume that environments are static. Such a strong assumption limits the application of most visual SLAM systems. The dynamic objects will cause many wrong data associations during the SLAM proces...
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Jiangying Qin, Ming Li, Xuan Liao and Jiageng Zhong
Oriented feature from the accelerated segment test (oFAST) and rotated binary robust independent elementary features (rBRIEF) SLAM2 (ORB-SLAM2) represent a recognized complete visual simultaneous location and mapping (SLAM) framework with visual odometry...
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