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- W4200205411 abstract "Most existing algorithms solve the visual odometry problem in dynamic scenes by removing dynamic objects. However, some scenarios such as autonomous driving, visual surveillance and virtual reality, require explicit motion information of the objects to help making decisions and understanding scenes. In this paper, we propose DMVO (Dymamic Multi-motion Visual Odometry), a robust and effective visual odometry algorithm that estimate camera pose as well as motion of objects in dynamic scenes using information from stereo cameras. DMVO combines disparity and depth estimation networks to obtain high-precision depth information, which improves the accuracy of the system. Local pose graph and global factor graph of dynamic and static structure are constructed for optimization. The DMVO system is tested on an indoor Oxford multi-motion dataset and an outdoor KITTI tracking dataset. The results show that our system achieves improved performance compared with the state-of-the-art algorithms." @default.
- W4200205411 created "2021-12-31" @default.
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- W4200205411 date "2021-10-20" @default.
- W4200205411 modified "2023-10-17" @default.
- W4200205411 title "DMVO: A Multi-motion Visual Odometry for Dynamic Environments" @default.
- W4200205411 doi "https://doi.org/10.1109/wcsp52459.2021.9613336" @default.
- W4200205411 hasPublicationYear "2021" @default.
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