  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
