论文标题
基于强大的循环生成的对抗网络和多任务学习的高精度自我监督的单眼视觉探空仪
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
论文作者
论文摘要
本文提出了一个高精度的自我监督的单眼VO，该VO是专为雾气天气而设计的。循环生成的对抗网络旨在通过强迫向前和向后的半周期进行一致的估计来获得高质量的自我监督损失。此外，引入了基于梯度的损失和感知损失，以消除复杂的光度变化对雾气天气中自我监督损失的干扰。为了解决深度估计问题不足的问题，根据深度估计与雾气天气中朦胧图像的深度估计和传输图计算之间的强相关性，设计了一个自我监督的多任务学习辅助深度估计模块。合成雾中的Kitti数据集的实验结果表明，与文献中其他最先进的单眼VO相比，提出的自我监督的单眼VO在深度和姿势估计上的表现更好，这表明该方法更适合于雾气。
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.
