Dark Reciprocal-Rank: Boosting Graph-Convolutional Self-Localization Network via Teacher-to-student Knowledge Transfer

Understand

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.