  In visual robot self-localization, graph-based scene representation and
matching have recently attracted research interest as robust and discriminative
methods for selflocalization. Although effective, their computational and
storage costs do not scale well to large-size environments. To alleviate this
problem, we formulate self-localization as a graph classification problem and
attempt to use the graph convolutional neural network (GCN) as a graph
classification engine. A straightforward approach is to use visual feature
descriptors that are employed by state-of-the-art self-localization systems,
directly as graph node features. However, their superior performance in the
original self-localization system may not necessarily be replicated in
GCN-based self-localization. To address this issue, we introduce a novel
teacher-to-student knowledge-transfer scheme based on rank matching, in which
the reciprocal-rank vector output by an off-the-shelf state-of-the-art teacher
self-localization model is used as the dark knowledge to transfer. Experiments
indicate that the proposed graph-convolutional self-localization network can
significantly outperform state-of-the-art self-localization systems, as well as
the teacher classifier.
