Camera relocalization has various applications in autonomous driving. Previous camera pose regression models consider only ideal scenarios where there is little environmental perturbation. To deal with challenging driving environments that may have changing seasons, weather, illumination, and the presence of unstable objects, we propose RobustLoc, which derives its robustness against perturbations from neural differential equations. Our model uses a convolutional neural network to extract feature maps from multi-view images, a robust neural differential equation diffusion block module to diffuse information interactively, and a branched pose decoder with multi-layer training to estimate the vehicle poses. Experiments demonstrate that RobustLoc surpasses current state-of-the-art camera pose regression models and achieves robust performance in various environments. Our code is released at: https://github.com/sijieaaa/RobustLoc

翻译：相机重定位在自动驾驶中具有多种应用。以往的相机位姿回归模型仅考虑环境扰动较小的理想场景。为应对可能包含季节变化、天气变化、光照变化以及不稳定物体存在的挑战性驾驶环境，我们提出RobustLoc，其通过神经微分方程获得对扰动的鲁棒性。我们的模型使用卷积神经网络从多视角图像中提取特征图，利用鲁棒神经微分方程扩散块模块来交互式地扩散信息，并通过带有多层训练的分支位姿解码器来估计车辆位姿。实验表明，RobustLoc超越了当前最先进的相机位姿回归模型，并在各种环境中实现了鲁棒性能。我们的代码已开源在：https://github.com/sijieaaa/RobustLoc