  We aim for domestic robots to perform long-term indoor service. Under the
object-level scene dynamics induced by daily human activities, a robot needs to
robustly 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 the
long-term deployment of the robot. This paper proposes a SEmantic understANding
Network (SeanNet) architecture that enables an effective learning process with
coupled visual and semantic inputs. With a dataset that contains object
dynamics, we propose a cascaded contrastive learning scheme to train the
SeanNet for learning a vector scene embedding. Subsequently, we can measure the
similarity between the current observed scene and the target scene, whereby
enables robust localization under object-level dynamics. In our experiments, we
benchmark SeanNet against state-of-the-art image-encoding networks (baselines)
on scene similarity measures. The SeanNet architecture with the proposed
training method can achieve an 85.02\% accuracy which is higher than baselines.
We further integrate the SeanNet and the other networks as the localizers into
a visual navigation application. We demonstrate that SeanNet achieves higher
success rates compared to the baselines.
