  In this paper, we address the problem of image sequence-based
self-localization from a new highly compressive scene representation called
sequential semantic scene graph (S3G). Highly-compressive scene representation
is essential for robots to perform long-term and huge-numbers of VPR tasks in
virtual-training and real-deploy environments. Recent developments in deep
graph convolutional neural networks (GCNs) have enabled a highly compressive
visual place classifier (VPC) that can use a scene graph as the input modality.
However, in such a highly compressive application, the amount of information
lost in the image-to-graph mapping is significant and can damage the
classification performance. To address this issue, we propose a pair of
similarity-preserving mappings, image-to-nodes and image-to-edges, such that
the nodes and edges act as absolute and relative features, respectively, that
complement each other. Moreover, the proposed GCN-VPC is applied to a new task
of viewpoint planning of the query image sequence, which contributes to further
improvement in the VPC performance. Experiments using the public NCLT dataset
validated the effectiveness of the proposed method.
