For autonomous vehicles (AVs), visual perception techniques based on sensors like cameras play crucial roles in information acquisition and processing. In various computer perception tasks for AVs, it may be helpful to match landmark patches taken by an onboard camera with other landmark patches captured at a different time or saved in a street scene image database. To perform matching under challenging driving environments caused by changing seasons, weather, and illumination, we utilize the spatial neighborhood information of each patch. We propose an approach, named RobustMat, which derives its robustness to perturbations from neural differential equations. A convolutional neural ODE diffusion module is used to learn the feature representation for the landmark patches. A graph neural PDE diffusion module then aggregates information from neighboring landmark patches in the street scene. Finally, feature similarity learning outputs the final matching score. Our approach is evaluated on several street scene datasets and demonstrated to achieve state-of-the-art matching results under environmental perturbations.

基于自主车辆的视觉感知技术，利用摄像头等传感器在信息获取和处理方面发挥重要作用。我们提出了一种名为RobustMat的方法，利用神经微分方程的强鲁棒性，在困难的驾驶环境中实现了地标补丁的匹配，并在环境干扰下取得了最先进的匹配结果。

鲁棒地图：在挑战环境下的街道地标图块匹配的神经扩散