  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.
