基于生成对抗网络的地点识别算法
Addressing Challenging Place Recognition Tasks using Generative Adversarial Networks
摘要：
地点识别是同时定位与地图构建（SLAM）的重要组成部分。在严重的外观变化下，可靠的位置识别是一项困难的感知任务，因为同一个地方在早上、晚上或不同季节的感知非常不同。这项工作地址的地方识别作为一个域翻译任务。使用一对耦合的生成对抗网络（GANs），我们证明了可以从另一个域（如冬天）生成一个域（如夏天）的外观，而不需要跨域的图像到图像的对应关系。域之间的映射是从每个域中的图像集合中学习的，而不需要通过强制执行循环一致性约束来知道实例到实例的对应关系。在这个过程中，为每个域学习有意义的特征空间，其中的距离可以用于位置识别任务。实验表明，学习的特征对应于视觉相似性，可以有效地用于跨季节的地点识别。
英文摘要：
Place recognition is an essential component of Simultaneous Localization And Mapping (SLAM). Under severe appearance change, reliable place recognition is a difficult perception task since the same place is perceptually very different in the morning, at night, or over different seasons. This work addresses place recognition as a domain translation task. Using a pair of coupled Generative Adversarial Networks (GANs), we show that it is possible to generate the appearance of one domain (such as summer) from another (such as winter) without requiring image-to-image correspondences across the domains. Mapping between domains is learned from sets of images in each domain without knowing the instance-to-instance correspondence by enforcing a cyclic consistency constraint. In the process, meaningful feature spaces are learned for each domain, the distances in which can be used for the task of place recognition. Experiments show that learned features correspond to visual similarity and can be effectively used for place recognition across seasons.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
