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We aim for domestic robots to perform long-term indoor service. Under theobject-level scene dynamics induced by daily human activities, a robot needs torobustly localize itself in the environment subject to scene uncertainties.Previous works have addressed visual-based localization in static environments,yet the object-level scene dynamics challenge existing methods for thelong-term deployment of the robot. This paper proposes a SEmantic understANdingNetwork (SeanNet) architecture that enables an effective learning process withcoupled visual and semantic inputs. With a dataset that contains objectdynamics, we propose a cascaded contrastive learning scheme to train theSeanNet for learning a vector scene embedding. Subsequently, we can measure thesimilarity between the current observed scene and the target scene, wherebyenables robust localization under object-level dynamics. In our experiments, webenchmark SeanNet against state-of-the-art image-encoding networks (baselines)on scene similarity measures. The SeanNet architecture with the proposedtraining method can achieve an 85.02\% accuracy which is higher than baselines.We further integrate the SeanNet and the other networks as the localizers intoa visual navigation application. We demonstrate that SeanNet achieves highersuccess rates compared to the baselines.

SeanNet: Semantic Understanding Network for Localization Under Object Dynamics