A high-precision self-supervised monocular visual odometry in foggy weather based on robust cycled generative adversarial networks and multi-task learning aided depth estimation. (arXiv:2203.04812v1 [cs.CV])

This paper proposes a high-precision self-supervised monocular VO, which is
specifically designed for navigation in foggy weather. A cycled generative
adversarial network is designed to obtain high-quality self-supervised loss via
forcing the forward and backward half-cycle to output consistent estimation.
Moreover, gradient-based loss and perceptual loss are introduced to eliminate
the interference of complex photometric change on self-supervised loss in foggy
weather. To solve the ill-posed problem of depth estimation, a self-supervised
multi-task learning aided depth estimation module is designed …

arxiv cv generative adversarial networks learning multi-task learning networks precision weather