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Deep Online Correction for Monocular Visual Odometry
[article]
2021
arXiv
pre-print
In this work, we propose a novel deep online correction (DOC) framework for monocular visual odometry. The whole pipeline has two stages: First, depth maps and initial poses are obtained from convolutional neural networks (CNNs) trained in self-supervised manners. Second, the poses predicted by CNNs are further improved by minimizing photometric errors via gradient updates of poses during inference phases. The benefits of our proposed method are twofold: 1) Different from online-learning
arXiv:2103.10029v1
fatcat:ngotxv2mzzbt3ii3vnrecaz3jy