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广泛应用。现有相机位姿回归模型仅考虑环境扰动较小的理想场景。为应对包含季节变化、天气、光照变化及不稳定物体等挑战性驾驶环境，我们提出鲁棒定位（RobustLoc）模型——通过神经微分方程赋予其对扰动的鲁棒性。该模型采用卷积神经网络从多视图图像中提取特征图，通过鲁棒神经微分方程扩散块模块实现信息的交互式扩散，并借助多层级训练的分支式位姿解码器估计车辆位姿。实验表明，鲁棒定位（RobustLoc）超越现有最先进的相机位姿回归模型，在多类环境中均展现出鲁棒性能。代码已开源：https://github.com/sijieaaa/RobustLoc