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For autonomous vehicles (AVs), visual perception techniques based on sensorslike cameras play crucial roles in information acquisition and processing. Invarious computer perception tasks for AVs, it may be helpful to match landmarkpatches taken by an onboard camera with other landmark patches captured at adifferent time or saved in a street scene image database. To perform matchingunder challenging driving environments caused by changing seasons, weather, andillumination, we utilize the spatial neighborhood information of each patch. Wepropose an approach, named RobustMat, which derives its robustness toperturbations from neural differential equations. A convolutional neural ODEdiffusion module is used to learn the feature representation for the landmarkpatches. A graph neural PDE diffusion module then aggregates information fromneighboring landmark patches in the street scene. Finally, feature similaritylearning outputs the final matching score. Our approach is evaluated on severalstreet scene datasets and demonstrated to achieve state-of-the-art matchingresults under environmental perturbations.

RobustMat: Neural Diffusion for Street Landmark Patch Matching under Challenging Environments