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DeepSLAM: A Robust Monocular SLAM System with Unsupervised Deep Learning

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Academic year: 2021

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Figure

Fig. 1: (a) Testing framework of the proposed DeepSLAM.
Fig. 2: Training scheme of the proposed DeepSLAM system. We use stereo images to train the system in order to recover the scale information of the environment which is similar to ones used in training
Fig. 4: Pose Graph with local and global connections. The dotted lines represent the global loops detected from  Loop-Net, while the solid lines represent the local loops generated by Tracking-Net
TABLE I: Tracking results on KITTI dataset with our proposed DeepSLAM system.
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