Relational graph location network for multi-view image localization

In multi-view image localization task, the features of the images captured from different views should be fused properly. This paper considers the classification-based image localization problem. We propose the relational graph location network (RGLN) to perform this task. In this network, we propose a heterogeneous graph construction approach for graph classification tasks, which aims to describe the location in a more appropriate way, thereby improving the expression ability of the location representation module. Experiments show that the expression ability of the proposed graph construction approach outperforms the compared methods by a large margin. In addition, the proposed localization method outperforms the compared localization methods by around 1.7% in terms of meter-level accuracy.