We propose a novel image based localization system using graph neural networks (GNN). The pretrained ResNet50 convolutional neural network (CNN) architecture is used to extract the important features for each image. Following, the extracted features are input to GNN to find the pose of each image by either using the image features as a node in a graph and formulate the pose estimation problem as node pose regression or modelling the image features themselves as a graph and the problem becomes graph pose regression. We do an extensive comparison between the proposed two approaches and the state of the art single image localization methods and show that using GNN leads to enhanced performance for both indoor and outdoor environments.

翻译：我们建议使用图形神经网络(GNN)建立一个基于新图像的本地化系统。预先培训的ResNet50进化神经网络(CNN)架构被用来为每张图像提取重要特征。随后,提取的特征被输入GNN, 以便通过将图像特征作为图中的节点来发现每个图像的外形, 并形成构成估计问题, 因为节点会形成回归, 或将图像本身建模成图, 而问题会变成图状会形成回归。 我们广泛比较了两种拟议方法, 以及艺术的单一图像本地化方法, 并表明使用GNNN可以提高室内和室外环境的性能。