Authors:
Yoshida Mitsuki
;
Yamamoto Ryogo
;
Wakayama Kazuki
;
Hiroki Tomoe
and
Tanaka Kanji
Affiliation:
Graduate School of Engineering, University of Fukui, Fukui, Japan
Keyword(s):
Active Cross-Domain Self-Localization, Semantic Scene Graph, Scene Graph Classifier, Scene Graph Embedding.
Abstract:
In visual robot self-localization, semantic scene graph (S2G) has attracted recent research attention as a valuable scene model that is robust against both viewpoint and appearance changes. However, the use of S2G in the context of active self-localization has not been sufficiently explored yet. In general, an active self-localization system consists of two essential modules. One is the visual place recognition (VPR) model, which aims to classify an input scene to a specific place class. The other is the next-best-view (NBV) planner, which aims to map the current state to the NBV action. We propose an efficient trainable framework of active self-localization in which a graph neural network (GNN) is effectively shared by these two modules. Specifically, first, the GNN is trained as a S2G classifier for VPR in a self-supervised learning manner. Second, the trained GNN is reused as a means of the dissimilarity-based embedding to map an S2G to the fixed-length state vector. To summarize,
our approach uses the GNN in two ways: (1) passive single-view self-localization, (2) knowledge transfer from passive to active self-localization. Experiments using the public NCLT dataset have shown that the proposed framework outperforms other baseline self-localization methods.
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