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.

翻译：对于自动驾驶汽车而言，基于摄像头等传感器的视觉感知技术在信息获取与处理中至关重要。在自动驾驶汽车的各种计算机感知任务中，将车载摄像头拍摄的地标块与不同时间捕获或存储于街景图像数据库中的其他地标块进行匹配可能具有重要价值。为应对季节、天气和光照变化等挑战性驾驶环境下的匹配任务，本文利用每个地标块的空间邻域信息，提出了一种名为RobustMat的方法，该方法通过神经微分方程增强对扰动的鲁棒性。具体而言，采用卷积神经ODE扩散模块学习地标块的特征表示，进而通过图神经PDE扩散模块聚合街景中相邻地标块的邻域信息，最终通过特征相似性学习输出匹配得分。我们在多个街景数据集上进行了评估，结果表明该方法在环境扰动下实现了最先进的匹配性能。