The objective of this paper is to address the localization problem using omnidirectional images captured by a catadioptric vision system mounted on the robot. For this purpose, we explore the potential of Siamese Neural Networks for modeling indoor environments using panoramic images as the unique source of information. Siamese Neural Networks are characterized by their ability to generate a similarity function between two input data, in this case, between two panoramic images. In this study, Siamese Neural Networks composed of two Convolutional Neural Networks (CNNs) are used. The output of each CNN is a descriptor which is used to characterize each image. The dissimilarity of the images is computed by measuring the distance between these descriptors. This fact makes Siamese Neural Networks particularly suitable to perform image retrieval tasks. First, we evaluate an initial task strongly related to localization that consists in detecting whether two images have been captured in the same or in different rooms. Next, we assess Siamese Neural Networks in the context of a global localization problem. The results outperform previous techniques for solving the localization task using the COLD-Freiburg dataset, in a variety of lighting conditions, specially when using images captured in cloudy and night conditions.

翻译：本文旨在解决利用安装在机器人上的折反射视觉系统捕获的全向图像进行定位的问题。为此，我们探索了使用全景图像作为唯一信息来源的孪生神经网络在室内环境建模方面的潜力。孪生神经网络的特点在于能够生成两个输入数据之间的相似度函数，在本文中即两个全景图像之间的相似度。本研究采用了由两个卷积神经网络（CNNs）组成的孪生神经网络。每个CNN的输出是一个用于表征图像特征的描述符。通过计算这些描述符之间的距离来衡量图像之间的差异度。这一特性使得孪生神经网络特别适用于执行图像检索任务。首先，我们评估了一项与定位密切相关的初始任务：检测两幅图像是否在同一房间或不同房间拍摄。接着，我们在全局定位问题的背景下评估了孪生神经网络的性能。实验结果表明，在使用COLD-Freiburg数据集解决定位任务时，该方法在多种光照条件下（特别是在多云和夜间条件下捕获的图像）的表现均优于现有技术。