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Multi-scale Deep CNN Network for Unsupervised Monocular Depth Estimation

Depth estimation problem is one of the most critical issues for robot SLAM.In this paper, the current unsupervised depth estimation method is mainly focused on single-scale features in CNN convolutional network architecture, and does not make full use of multi-scale features along the network.In addition, the design of the loss function for depth estimation is also adapted to the single scale.In this paper, we also propose a multi-scale unsupervised depth estimation for the above problems, and introduce a matching loss function to adapt to the training of multi-scale networks. Experiments were carried out on the KITTI dataset. The experimental results show that the proposed method improves the accuracy and efficiency of depth estimation.