  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 based on the
strong correlation between the depth estimation and transmission map
calculation of hazy images in foggy weather. The experimental results on the
synthetic foggy KITTI dataset show that the proposed self-supervised monocular
VO performs better in depth and pose estimation than other state-of-the-art
monocular VO in the literature, indicating the designed method is more suitable
for foggy weather.
