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- W3201852296 abstract "In recent years, visual SLAM has achieved great progress and development, but in complex scenes, especially rotating scenes, the error of mapping will increase significantly, and the slam system is easy to lose track. In this article, we propose an InterpolationSLAM framework, which is a visual SLAM framework based on ORB-SLAM2. InterpolationSLAM is robust in rotating scenes for Monocular and RGB-D configurations. By detecting the rotation and performing interpolation processing at the rotated position, pose of the system can be estimated more accurately at the rotated position, thereby improving the accuracy and robustness of the SLAM system in the rotating scenes. To the best of our knowledge, it is the first work combining the interpolation network into a Visual SLAM system to improve SLAM system robustness in rotating scenes. We conduct experiments both on KITTI Monocular and TUM RGB-D datasets. The results demonstrate that InterpolationSLAM outperforms the accuracy of standard Visual SLAM baselines." @default.
- W3201852296 created "2021-10-11" @default.
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- W3201852296 date "2021-10-06" @default.
- W3201852296 modified "2023-09-27" @default.
- W3201852296 title "InterpolationSLAM: A Novel Robust Visual SLAM System in Rotating Scenes." @default.
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