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- W4312388011 abstract "Current visual SLAM algorithms usually assume the working environment is static, and may suffer from large localization errors in high dynamic scenes. This paper proposes an efficient and robust RGB-D visual SLAM scheme, which uses the GCNv2 network to perform feature extraction and combines with the Grid-based Motion Statistics(GMS) algorithm to perform robust feature matching. Compared with traditional methods, it is less prone to feature loss and improves the reliability of visual front-end pose tracking. What's more, considering the high dynamic characteristics of the actual scenes, lightweight semantic segmentation network FcHarDNet and multi-view geometric constraints are combined to remove the feature points belonging to dynamic objects and retain static feature points to participate in pose estimation, which reduces the impact of dynamic objects in the visual SLAM system. On top of that and based on the prior semantic information, this paper successfully constructs static dense point cloud maps without dynamic objects. Finally, experiments are conducted on the existing public datasets. Experimental results show that the proposed scheme is robust to pose tracking and can greatly improve the localization accuracy in a dynamic environment." @default.
- W4312388011 created "2023-01-04" @default.
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- W4312388011 date "2022-08-04" @default.
- W4312388011 modified "2023-09-27" @default.
- W4312388011 title "A RGB-D Visual SLAM Algorithm Based on GCNv2 and GMS for Dynamic Scenes" @default.
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- W4312388011 doi "https://doi.org/10.1145/3556384.3556424" @default.
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